Social Design Engineering Series

SDES-2024-4

Does future design induce people to make a persistent change
to sustainable food consumption?
Rahman Md. Mostafizur
School of Economics and Management, Kochi University of Technology
Department of Agricultural Marketing and Business Management, Sylhet Agricultural University

Khatun Mst. Asma
Research Institute for Future Design, Kochi University of Technology
Department of Agricultural and Applied Statistics, Bangladesh Agricultural University

Moinul Islam
School of Economics and Management, Kochi University of Technology
Research Institute for Future Design, Kochi University of Technology

Tatsuyoshi Saijo
Institute for International Academic Research, Kyoto University of Advanced Science

Koji Kotani
School of Economics and Management, Kochi University of Technology
Research Institute for Future Design, Kochi University of Technology
Urban Institute, Kyushu University
College of Business, Rikkyo University

July 3, 2024
School of Economics and Management
Research Institute for Future Design
Kochi University of Technology
KUT-SDE working papers are preliminary research documents published by the School of Economics and Management jointly with the ResearchInstitute for Future Design at Kochi University of Technology. To facilitate prompt distribution, they have not been formally reviewed and edited.
They are circulated in order to stimulate discussion and critical comment and may be revised. The views and interpretations expressed in these papers
are those of the author(s). It is expected that most working papers will be published in some other form.

Does future design induce people to make a persistentchange to sustainable food consumption?
Rahman Md. Mostafizur*,† Khatun Mst. Asma‡,§ Moinul Islam*,‡
Tatsuyoshi Saijo¶ Koji Kotani*,‡,||, **,††
July 3, 2024

Abstract
It is crucial whether or not people make sustainable food consumption (SFC) for resolving climate change, health and environmental problems as well as achieving SDGs. However,
little is known about what encourages people to make a sustained shift to SFC. This research
considers a future design (FD) approach where people are asked to think of a problem and takeactions through taking a perspective of future generations, investigating the question “howdoes the FD approach impact food consumption?” and the hypothesis “FD induces a lasting
shift to SFC.” We employ a social experiment with three treatments of “baseline,” “deliberation” and “FD,” collecting data on organic and nonorganic vegetable consumptions with 300
households in Bangladesh over three months. In baseline, households report the consumptions.
In deliberation, they additionally deliberate among their family members to think of a vision, amission and a strategy for the consumptions. In FD, they additionally take each perspective ofpast, current and future generations and then deliberate to think of the same issues. The result
indicates that FD affects people to have a sustained increase (decrease) in organic (nonorganic)
vegetable consumption as compared to any other treatment, and the effect under FD is approximately twice as much as that under deliberation in magnitude and in each round. Overall, FD
demonstrates a great potential for inducing people to make a persistent change to SFC.

Keywords: Sustainable food consumption; Organic vegetables; Future design; Deliberation; Social experiment; Bangladesh* School of Economics and Management, Kochi University of Technology†

Department of Agricultural Marketing and Business Management, Sylhet Agricultural University
Research Institute for Future Design, Kochi University of Technology§
Department of Agricultural and Applied Statistics, Bangladesh Agricultural University¶
Institute for International Academic Research, Kyoto University of Advanced Science||
Urban Institute, Kyushu University** College of Business, Rikkyo University††
Corresponding author, E-mail: kojikotani757@gmail.com‡

1

Contents
1

Introduction

3

2

Methods
7
2.1 Study areas and sampling strategy . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Experimental setup and procedures . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Statistical analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3

Results

17

4

Conclusion

27

5

Appendix

28

6

Acknowledgments

28

Nomenclature
AATT Aggregated average treatment effect on the treated
ATT

Average treatment effects on the treated

BDT Taka, Bangladeshi currency
DALYs Disability-adjusted life-years
DID

Difference-in-difference

FD

Future design

GHG Greenhouse gas
glm

Generalized linear model

GSEM Generalized structural equation model
NVCs Nonorganic vegetable consumptions
OVCs Organic vegetable consumptions
RAs

Research assistants

SDGs Sustainable development goals
SFC

Sustainable food consumption

TEM Total earning members
TFM Total family members under 18

2

1

1

Introduction

2

Climate change substantially threatens ecological systems, intensifies severe weather events,

3

reduces biodiversity and presents complex risks to our current societal framework (O’neill et al.,42017). The food system is responsible for more than a third of greenhouse gas (GHG) emissions,5

directly contributing to climate change (Reisch et al., 2013, Mbow et al., 2020, Crippa et al., 2021).

6

Food is a crucial link between human wellbeing and the planet’s health (Sánchez et al., 2021).

7

Household consumptions bear approximately 60 % of global GHG emissions and food is among8

the most impactful consumption categories in terms of emissions (Wellesley et al., 2015, Ivanova

9

et al., 2016). At the same time, the dietary factor plays a significant role in worldwide burdens of

10

diseases, resulting in 11 million deaths and 255 million disability-adjusted life-years (DALYs) in112017 (Afshin et al., 2021). Ample evidence exists that nearly every dimension of human health is12

also affected by food practices and the disease burdens along with aggregate ecosystem alterations

13

have been growing (Myers et al., 2013). In order to mitigate ongoing problems with climate change

14

and health, it is crucial to transition our food consumptions to be sustainable, i.e., sustainable food15

consumption (SFC) practices (Vittersø and Tangeland, 2015). There are several studies that have16

examined the determinants of short-term or immediate changes in food consumptions (Vecchio

17and Cavallo, 2019, Thøgersen, 2000). This research seeks to experimentally address some possible

18

medium-term or long-term behavioral changes to SFC.

19

Jackson (2006) argues that a SFC concept has emerged recently and it is attributed to the20

Agenda 21 document, a significant policy suggestion from the United Nations Earth Summit held

21

in Rio in 1992. The SFC is defined to be the use of food products “that respond to basic needs

22and bring a better quality of life while minimizing the use of natural resources, toxic materials and23

emissions of waste and pollutants over the life cycle, so as not to jeopardize the needs of future

24

generations” (Oslo Ministerial Roundtable, 1994). Extensive scientific research and public opinion

25

indicate that current rates and methods of food production and consumption are unsustainable, and26

such failures for sustainability have resulted in or will result in irreversible negative consequences

27

for future societal wellbeing (Pollard et al., 2010, Grooten and Almond, 2018, Kemper and Ballan328tine, 2019). Some food consumption is viewed as an effective way towards achieving a transition29

to SFC, such as an increase in organic and/or plant-based food consumptions and/or reduction in

30

specific meat consumptions (Hoek et al., 2004, Lea et al., 2006, Hughner et al., 2007, Vittersø and31

Tangeland, 2015).

32

Human behaviors are argued to be explained via dual-process theories in cognition (Evans,

33

2008, Kahneman, 2011). The theory posits two distinct cognitive processes that govern decisions

34

and behaviors, i.e., automatic and deliberative ones. The automatic processes are concerned with35

peripheral routes of information processing, while the deliberative ones are concerned with the36central routes that require some cognitive efforts. Thus, the deliberative one is known to be asso-

37

ciated with languages and with the capacity to think about future and counterfactual possibilities.

38

It is demonstrated that some interventions on the automatic and deliberative processes can have

39

short-term or immediate effects across different domains, while the long-term effects are not well40

documented (Allcott and Rogers, 2014, Abrahamse, 2020). Such long-term behavioral changes

41

are known to be achieved through habit formations, a change in mental contents and cognition,

42

recursive social exchanges and benefit calculation in deliberative cognitive processes (Rogers and43

Frey, 2015, Volpp and Loewenstein, 2020). Rothman et al. (2009) suggests that interventions on

44

deliberative processes shall be effective at maintaining a long-term behavioral change, requiring

45

systematic data collection over sufficiently long time.

46

Several field and lab experiments have examined some impacts of interventions on automatic

47

processes, such as nudging, labels and visual prompts, in a variety of contexts to alter people’s

48

consumption behaviors (Maas et al., 2012, Whitehair et al., 2013, Bucher et al., 2016, Brunner49

et al., 2018, Hummel and Maedche, 2019, Abrahamse, 2020, Majer et al., 2022, Qi et al., 2022,

50

Segovia et al., 2023). A field experiment by Vandenbroele et al. (2018) investigates whether or not

51

introducing some small-sized commodity to a retailer’s assortment encourages consumers to buy,

52

finding that it successfully nudges consumers to do so. Lohmann et al. (2022) estimate the causal

53

effect of some labels on individual food choices through conducting experiments at cafeterias in54

five different universities, revealing that carbon footprint labels promote climatarian diets. Another4

55

set of studies has carried out a series of experiments to investigate the immediate effects of inter-

56

ventions on deliberative processes by noting descriptive information provisions and social norms

57

to subjects, confirming the existence of such effects on people’s decisions and behaviors (Nomura

58

et al., 2011, Monroe et al., 2015, Sparkman and Walton, 2017, Stöckli et al., 2018, Nguyen et al.,59

2022, Bazoche et al., 2023). A study by Carfora et al. (2019) tests the impact of different messag-

60

ing interventions on people’s attitudes and behaviors about red and processed meat consumptions

61

in field experiments with 350 university students in Italy. They suggest that health and environ-62

mental messages have some prolonged effects even after one month of the interventions. Overall,

63

the literature generally establishes that some interventions on automatic and deliberative processes

64

can have short-term effects on consumption behaviors.

65

Future studies approaches, i.e., visioning, backcasting and scenario planning, have been stud-66

ied in the past few decades to depict how a group of people or organizations adapt to anticipated67

future trends and to provide insights into opportunities for changes in behaviors and strategies

68(Swart et al., 2004, Bell, 2009, Phdungsilp, 2011, Amer et al., 2013, Timilsina et al., 2020). Most

69

of these future studies approaches utilize visioning to share and understand a thorough, robust

70

and consensus-based vision among participants in the workshops (Potschin et al., 2010, Costanza,71

2000, Wiek and Iwaniec, 2014). Prior research has applied backcasting and/or scenario plan-72

ning with visioning in various domains, such as businesses, natural resource management and73

economies in a society, for various adaptations and changes (Phdungsilp, 2011, Pereverza et al.,74

2019, Thorén and Vendel, 2019, Sandström et al., 2020). It is claimed that there is little empirical75

evidence regarding how much such future studies approaches impact people’s or organizational

76

behaviors and strategies (Oliver, 2023). A new approach called future design (FD) has gained at-77

tention and research interest, empirically establishing the impact on the behaviors and strategies

78

in experiments (Saijo, 2020, Timilsina et al., 2020, Pandit et al., 2021, Shahen et al., 2021). How-79

ever, the impacts have yet to be proven to persist in the medium or long-term. FD comprises a

80

series of procedures where people read a case-method material and think about a problem by tak-81

ing each perspective of past, current and future (Nakagawa et al., 2019, Pandit et al., 2021). After5

82that they take the perspective of future generations, make deliberations and request some visions,83

missions and strategies to current generations. Finally, taking the current generations perspective,

84

people suggest their own visions, missions and strategies for the problem. To enhance intergen-85

erational sustainability, researchers introduce future ahead and back (FAB) and intergenerational

86

accountability mechanisms as part of FD approaches (Shahen et al., 2021, Timilsina et al., 2023).

87

MacAskill (2022) also proposes a similar idea to introduce “longtermism” as a view that we should88

do much more to protect the interests of future generations, recognizing that our current actions

89

will have a significant impact on countless future people. Despite that, FD offers a series of proce-90

dures to address a problem, developing self accountability through visions, missions and strategies

91

and taking the perspective of different times.

92

Previous research has primarily focused on examining the immediate or short-term changes

93

in people’s behaviors. Little studies have documented sustained behavioral changes and studied94

how some interventions, such as future studies approaches, affect people in the medium or long95

run. Some recent researches have presented a great potential of FD approach to induce people to96

make long-term changes in their behaviors, demonstrating its strong effectiveness for short-term

97changes through conducting laboratory and field experiments (Timilsina et al., 2020, Pandit et al.,98

2021, Shahen et al., 2021, Timilsina et al., 2023). In this study, we focus on examining possible

99

impacts on people’s food consumption behaviors by FD interventions as an experimental study

100

for long-term behavioral changes, because the persistent shift to SFC is known to be crucial for

101

addressing climate change and health issues at global level. Therefore, we pose a question “how102

does the FD approach impact food consumption,” hypothesizing that FD induces a lasting shift103

to SFC. We design and institute a 3-round social experiment with three treatments of “baseline,”

104“deliberation” and “FD,” collecting data on organic and nonorganic vegetable consumptions with

105

300 households in Bangladesh over three months. Addressing the question and hypothesis will106

be beneficial for inducing people to make a persistent change to SFC in tackling issues related to107sustainable development goals (SDGs).

6

108

2

Methods109

2.1

Study areas and sampling strategy

110

We chose Jashore and Jhenaidah as our study areas and they are located in the south-western111

region of Bangladesh. Figure 1 illustrates the study areas which are recognized as a regional cen-112

ter of agriculture, particularly, vegetable production (BBS, 2022). People’s sociodemographic and113

ethnic profiles in the two areas are homogeneous and close to the country’s average (BBS, 2020).

114

In Bangladesh, there are some specific areas where local agricultural production meets certain re-

115

quirements for organic agriculture, and our study areas are the ones that engage on such organic

116

agriculture (Badgley et al., 2007, Rahmawati et al., 2018). Organic agriculture is a comprehensive

117

approach towards food production that aims to improve the health of agroecosystems including118

biodiversity, biological cycles and soil activities (Gomez and Thivant, 2017). The approach high-119

lights the utilization of natural inputs, such as minerals and plant-derived products, abstaining from120

use of synthetic fertilizers and pesticides (Singh and George, 2012, Ferdous et al., 2021). On the121

other hand, nonorganic agriculture involves modern agricultural practices that heavily rely on syn-122

thetic chemical fertilizers, fungicides, insecticides and herbicides (Campion et al., 2020). In the123

areas, the market prices of organic vegetables are comparable to those of nonorganic vegetables,124

and farmers can choose how much to produce organic and nonorganic vegetables depending on

125

their production environments, modes and methods in each growing season (FAO, 2023). There-126

fore, both organic and nonorganic vegetables are produced and traded in agricultural markets at127

the local vicinity, while local people have some experiences to purchase and consume organic

128

and nonorganic vegetables in the markets or from the farm gates as their daily practices (Dhaka

129

Tribune, 2014, Musa et al., 2015, Seraj, 2019). The features in our study areas are considered130

appropriate for conducting our social experiments.

131

Three treatments, baseline, deliberation and future design (FD), are prepared for the experi-

132

ments over three rounds (t = 1, 2 and 3) between January 2023 and March 2023 that consists of133

one pretreatment round and two posttreatment rounds (figure 2a). The households were selected7

Figure 1: The study areas in Bangladesh, Jashore and Jhenaidah

8

134

by following the stratified random sampling techniques. We randomly selected 150 households in

135

each of Jashore and Jhenaidah areas by using the residence list and the random number generators,

136

totaling 300 households. The randomization procedures were also employed to assign 50 out of137

the 150 households to each of the three treatments within one area, and consequently, 100 house-138

holds are assigned to each treatment. The 1st author administered the experiments, organized and139

trained local support staff as well as research assistants for the households’ recruitment, participa-

140

tion and data collection, explaining our experiments as the programs organized by the university

141

and the Upazila agriculture office to the households. The households received an instruction for142

an overview of the programs and signed an informed consent for their participation in advance143(see the appendix material A for the experimental instructions). Therefore, they participated with-144

out noticing that they are part of our experiments, while the participation rate was approximately

145

85 %.1 It reflects that a majority of households are familiar with organic and nonorganic vegetables,

146

considering the program participation fee (= 900 BDT) to be good enough. The fees were solely

147

provided as the compensation for their involvement in the programs that contain three rounds of148

our experiments, and the amount was the same for all the households.

149

2.2

Experimental setup and procedures

150

The social experiments along with questionnaire surveys were conducted to collect neces-151

sary information of the households regarding their sociodemographic variables, daily organic and152

nonorganic vegetable consumptions in a week in each round to calculate their weekly organic

153

vegetable consumptions (OVCs) and nonorganic vegetable consumptions (NVCs) (figure 4). We

154

collect weekly vegetable consumptions data in each round, implicitly choosing a specific week155with an interval of one month. We do so to capture the persistent change precisely, considering the156

buying culture of Bangladeshi households, as well as the time and cost involved in conducting the1

We identified that some households rejected their participation due to several reasons, such as health problems.
However, we sought to recruit households for reaching a sample size of 300. The participation rate is considered theratio between the number of households we invited through sampling processes and the number of households who
actually participated in the programs.

9(a) The temporal flows of experiments over three rounds, pretreatment round 1, posttreatment round 1 and posttreatment round 2(b) The procedures in each of the 1st and 2nd experimental interventions for deliberation and future design (FD) treatments as compared to no intervention for baseline one

Figure 2: Experimental procedures over time and the flow of experimental interventions per treatment

10

157

research.2 The sociodemographic variables include the household head’s pretreatment knowledge

158

about organic food, age, gender, education, household income, a number of family members under

159

18 and earning members (see the appendix material B for collecting information of the pretreatmnt

160

knowledge). However, information regarding some time-invariant sociodemographic variables is161

collected only in the pretreatment round. The prices for OVCs and NVCs are not included as

162

independent variables, because they remain the same for households across treatments over three163

periods. In the field, there is a lack of access to computers or devices equipped with internet

164

connectivity. Consequently, all the procedures were conducted manually by the experimenter and165

research assistants. The households in the study were literate and employed traditional writing

166

instruments, namely pens and papers, to respond to the inquiries. Whenever the households had

167

questions, the experimenter or research assistants sought to answer them in a real-time manner.

168

Deliberation and future design (FD) treatments are set to examine the effects on household

169

food consumptions in each area on top of baseline one, consisting of the 1st and 2nd experimental

170

interventions between two rounds (figure 2b). With this design, we test a hypothesis “FD induces

171

a lasting shift to SFC as compared to the baseline and deliberation.” Households in baseline are172

asked to help in recording and reporting their OVCs and NVCs over three rounds without any

173

experimental intervention, being considered “untreated” over all periods. In deliberation and FD,

174

households receive some experimental interventions that consist of reading case-method materi-175

als, watching a video and discussing about their food consumptions regarding OVCs and NVCs

176

between the two rounds (figure 2b). Therefore, households in deliberation and FD are consid-

177

ered “untreated” in pretreatment round 1 (t = 1), and “treated” in posttreatment rounds 1 and 2

178(t = 2 and 3). The case-method materials provide households with a brief history, definitions and179

current situations of organic and nonorganic foods in their native language for easy comprehen2Households think that they are under monitoring all the weeks in each round without knowing that experimenters
implicitly choose a week for precisely monitoring and measuring food consumptions. At the same time, we admitthat there may be an alternative way to quantify a persistent change in food consumptions, such as trying to collectevery-day data over months without specifying a week. However, we conclude that these procedures become too
demanding for our research team as well as for households in terms of time and cost, and it shall be very difficult toprecisely collect all day consumption data. Overall, considering food cultures, practices and the nature of Bangladeshi
households, we do believe that the data-collection procedure we use in this research is one of the most effective ways
to precisely measure and quantify the persistent change by streamlining the burden, cost and time on several aspects.

11

180

sion (see the appendix C for case-method materials). In a video, scientific information and facts

181

for organic and nonorganic foods along with the detailed production and consumption processes in

182

Bangladesh are introduced over 10 minutes, referring to some books, reports and articles (Sarker

183

and Itohara, 2008, Yoshino, 2010, Zulfiqar and Thapa, 2016, Gomez and Thivant, 2017, Ferdous

184

et al., 2021, BBS, 2022).

185

In deliberation, household heads are first asked to write their initial suggestions for betterment

186

of household food consumptions after reading the case-method materials and watching a video

187(figure 2b). They are asked to sit for approximately 10 to 20 minutes of discussion with household

188

members and then to summarize the suggestions raised via the discussion. Ultimately, they are189

requested to write their household visions, missions and strategies as the final suggestions for the190

betterment. In FD, after reading case-method materials and watching a video, household heads are

191

asked to write their evaluations and understanding about food consumptions and organic agricul-

192

ture, taking each perspective of people in the past (2001), in the current (2023). Additionally, they193

are asked to imagine what will possibly happen or they should do on food consumptions in the194

future, taking both perspectives of people in the current (2023) and in the future (2043) to expand

195

their ways of thinking and viewpoints (Timilsina et al., 2020, Pandit et al., 2021, Shahen et al.,1962021) (figure 2b). When household heads and members take the future standpoint, they are asked

197

to think and write possible requests of visions, missions and strategies for food consumptions to the198

current as if they are people in the future (2043). Therefore, they discuss with household members

199

from the future standpoint and summarize the requests. Finally, household heads and members are

200

asked to get back to the current standpoint, writing their final suggestions of household visions,201

missions and strategies for food consumptions. In deliberation, household heads and members

202

complete every intervention only from their own current standpoint. However, in FD, household

203

heads and members go through different perspectives of people in the past, current and future204

to complete every intervention. In particular, they are asked to make the requests to the current

205

from the future standpoint, finally getting back to the current standpoint for finalizing their visions,206

missions and strategies.

12

207

We track and monitor weekly organic and nonorganic vegetable consumptions for 300 house-

208

holds in pretreatment round 1, posttreatment round 1 and posttreatment round 2 over three months.

209

Households are randomly monitored through visits by RAs or the 1st author over a month in each

210

round, and we sought to confirm how their vegetable purchases and consumptions are truthfully re-211

ported. In particular, we implicitly pick a specific week for intensive observations and monitoring212

of each household in each round across treatment to obtain precise OVCs and NVCs, and house-213

holds never know which week is that specific week. In pretreatment round 1, all households are214

requested to provide sociodemographic information by completing questionnaires and the weekly

215

vegetable consumptions without any experimental intervention (figure 2a). Just before posttreat-

216

ment round 1, the 1st experimental interventions take place for the households in deliberation and217

FD (see the 1st experimental interventions in figure 2a). After the 1st interventions, we track weekly

218

vegetable consumptions and randomly monitor each household over posttreatment round 1. In to-219tal, we conduct 12 sessions and organize a session of 20 to 30 households for the experimental

220

interventions in baseline, deliberation and FD treatments that take approximately 30, 80 and 100

221

minutes, respectively. Just before posttreatment round 2, the 2nd experimental interventions are222

made, following the same procedures as the 1st experimental interventions. After that, we keep

223

track of the consumptions and monitor each household over posttreatment round 2 as in posttreat-224

ment round 1. Upon completing the three rounds, a program participation fee was transferred to225

the bank accounts of the participating households.

226

2.3

Statistical analyses

227

The experimental panel data over three rounds of t = {1, 2, 3} from 300 households are or-228

ganized and utilized for the statistical analyses, consisting of OVCs, NVCs, treatment dummies

229

and sociodemographic variables (see table 1 for the definitions of all variables). To estimate the230treatment effects, we apply a difference-in-difference (DID) method with multiple-time periods

231

which is one of the most popular approaches to evaluate causal treatment effects (see, e.g., Angrist232

and Pischke, 2009, Wooldridge, 2010, Chakrabarti et al., 2018, Hossain et al., 2019, Callaway and13

14

a BDT stands for Bangladeshi currency “taka.”

Total family members under 18 (TFM)
Total earning members (TEM)

Age
GenderHousehold income
Education

Dependent variables
Organic vegetable consumptions (OVCs)
Nonorganic vegetable consumptions (NVCs)
Independent variables
Treatments (Base group = Baseline treatment)
Deliberation treatment
Future design treatment
Sociodemographic variables
Preknowledge

Variables

Household head’s pretreatment knowledge about organic food from 1 to 7(completely low = 1, mostly low = 2, somewhat low = 3,neither high nor low = 4, somewhat high = 5, mostly high = 6
and completely high = 7)
Household head’s age is expressed by years
It takes 1 when a subject is female, otherwise 0
Monthly income in BDTaHousehold head’s education from 0 to 6 (no formal education or no schooling = 0,
1 to 5 years of schooling = 1, 6 to 8 years of schooling = 2,
9 to 10 years of schooling or secondary school certificate = 3,
12 years of schooling or higher secondary school certificate = 4,
undergraduate university degree or equivalent = 5 and graduate degree or equivalent = 6)
Number of persons in a family living together under 18 years old
Number of earning persons in a family

It takes 1 when a household is in deliberation treatment, otherwise 0
It takes 1 when a household is in future design treatment, otherwise 0

Weekly consumptions of organic vegetable in kg
Weekly consumptions of nonorganic vegetable in kg

Descriptions

Table 1: Definitions of variables

233

Sant’Anna, 2021, Braghieri et al., 2022, Cameron and Trivedi, 2022, Kemigisha et al., 2023). With

234

this method, the pre-post difference in the mean vegetable (organic and nonorganic) household

235

consumptions in deliberation or FD is compared with that in baseline, and it enables to identify the236

aggregated average treatment effects on the treated denoted by AATTK
j for K = {OVCs, NVCs}

237

and j = {deliberation, FD} (Callaway and Sant’Anna, 2021).

238

239

The equations are specified as

AATTK
j =

3
X

ω(g, t) · ATTK
j (g, t)

(1)

t=2

240

241

YitK = γtK + γgK +K
YitK = λK
t + λg +

1
X
e=0
1
X

γeK deliberationeit + γxK Xi + uit

(2)

e
K
λK
e FDit + λx Xi + ϵit .

(3)

e=0

242

where g is the time period when a household first becomes treated, ω(g, t) is the weighting function

243

and ATTKj (g, t) is the average treatment effects on the treated (Callaway and Sant’Anna, 2021).

244

Households in baseline had remained untreated for all periods, being specified as a group by g = 0,245while those in deliberation and FD treatments get treated in the periods t = 2 and t = 3, being

246

specified as a group by g = 2. In equations (2) and (3), YitK s are the dependent variables of OVCs

247

and NVCs from households indexed by i = 1, . . . , 300, deliberationeit and FDeit are the treatment

248

dummy variables, taking a value of 1 when household i gets treated at time period t, e is the length249

of a treatment exposure (e = t − g), Xi is a vector of the pretreatment sociodemographic variables,

250

K
K
γtK and λK
t are parameters for time-fixed effects, γg and λg are parameters for group-fixed effects,

251

K
γxK and λK
x are vectors of parameters associated with Xi , uit and ϵit are error terms. Finally, γe252

and λKe are the main parameters of our interests to measure the average treatment effects across

253

K
K
different lengths of exposure to the treatment, i.e., ATTKdeli (2, 2) = E(γ0 ) and ATTdeli (2, 3) =

254

K
K
K
K
E(γ0K + γ1K ), ATTK
FD (2, 2) = E(λ0 ), ATTFD (2, 3) = E(λ0 + λ1 ).

255

Figure 3 is a conceptual framework that visualizes the relationships among groups, time pe-

256

riods, sociodemographic variables and households’ consumptions of organic and nonorganic veg-

15

Figure 3: A conceptual framework describing the relationships among group, time period, sociodemographic variables, organic vegetable consumptions (OVCs) and nonorganic vegetable consumpKK
K
K
tions (NVCs) where γgK , γtK , γeK , λK
g , λt , λe and γx , λx are coefficients and a vector coefficientsfor the corresponding factor; and K = {OVCs, NVCs}.

257

etables. The relationships among variables represented by plane arrows in figure 3 are statistically258

identified through estimating parameters in equations (1) to (3). Our focus is on estimating the

259

K
K
K
coefficients of λK
e , λg , λt and λx in figure 3 and on comparing the estimates with those in equa-260tion (2). Recall our research question “how does the FD approach impact food consumption?”and261

the hypothesis “FD induces a lasting shift to SFC.” The coefficients of λK
e s are the most important262

key parameters enabling us to answer the research question and the hypothesis. Specifically, the263

K
hypothesis can be posed as follows: H0 : λKe = 0 and H1 : λe ̸= 0 for e = {0, 1}. In this regard,264

we expect that households in FD increase (decrease) OVCs (NVCs) over the rounds as compared265

to those in baseline and deliberation or equivalently AATTKF D is estimated to be practically and266

statistically significant.

267

As a robustness check, we apply a two-part model with random effects using the probit-glm

268

framework (see, e.g., Belotti et al., 2015, Eisenberg et al., 2015, Farewell et al., 2017, Pallegedara,

269

2020, Jiang and Ni, 2020, Dangerfield et al., 2021, Amore and Murtinu, 2021, Kruse et al., 2021,

270

Cameron and Trivedi, 2022, Bazoche et al., 2023). In the first part, a probit model is estimated
16

271

for the probability of observing a positive OVC or a positive NVC versus zero. In the second

272

part, conditional on the positive consumption, a generalized linear model (glm) is estimated with a273

family of gamma and log links through validating a modified Park and Prigibon test, respectively

274(see, e.g., Polgreen and Brooks, 2012, Glick, 2015, Ng and Cribbie, 2017). The two-part model is

275

specified as276

Φ−1 Pr(YitK > 0|Wit ) = α0K + α1K deliberationit + α2K FDit + α3K Xit + ViK

(4)

277

log[E(YitK |YitK > 0, Wit )] = β0K + β1K deliberationit + β2K FDit + β3K Xit + UiK

(5)

278

where Φ−1 is the probit link function, YitK s are dependent variables of OVCs and NVCs for house-279

hold i at time period t, Wit is a vector of all the independent variables that follows, deliberationit

280

and FDit are treatment dummy variables that takes a value of 1 when household i is in deliberation

281

or FD at time period t, respectively, Xit is a vector of the pretreatment sociodemographic vari-282

ables, ViK and UiK are random intercepts, being assumed to be uncorrelated with Wit . The α1K ,283

α2K and α3K (β1K , β2K and β3K ) are parameters to be estimated for the probit model (glm), and our284

main concerns are on estimating α1K , α2K , β1K and β2K associated with deliberationit and FDit , con-285

trolling for other independent variables. We estimate the two-part model of equations (4) and (5)286for posttreatment round 1, posttreatment round 2 as well as for the panel data by applying the gen-287

eralized structural equation model (GSEM) approach (Jiang and Ni, 2020, Cameron and Trivedi,

2882022). Finally, the combined marginal effects for each independent variable in all models are com-289

puted with the estimated coefficients to quantify the magnitude of the impacts by deliberation and290

FD treatments on the dependent variables (Wooldridge, 2010, Jiang and Ni, 2020, Cameron and291

Trivedi, 2022).

292

3

Results

293

Table 2 reports the summary statistics of the major independent variables for households in294

baseline, deliberation, future design (FD) and overall sample, respectively. It is observed that the17Table 2: Summary statistics of the independent variables
Treatments

OverallBaseline

Deliberation

Future design

3.95 (4.38)
1.39
1.00
7.00

4.08 (4.25)
1.25
1.00
6.75

4.19 (4.75)
1.34
1.00
6.50

4.07 (4.50)
1.33
1.00
7.00

37.89 (35.00)
13.60
18.00
69.00

35.2 (35.00)
10.97
17.00
63.00

37.09 (35.00)
11.63
16.00
65.00

36.73 (35.00)
12.13
16.00
69.00

0.43 (0.00)
0.49
0.00
1.00

0.42 (0.00)
0.49
0.00
1.00

0.41 (0.00)
0.49
0.00
1.00

0.42 (0.00)
0.49
0.00
1.00

3.05 (3.00)
1.48
1.00
6.00

3.07 (3.00)
1.60
0.00
6.00

3.62 (4.00)
1.76
0.00
6.00

3.25 (3.00)
1.64
0.00
6.00

1.33 (1.00)
1.12
0.00
7.00

1.40 (1.00)
0.93
0.00
4.00

1.29 (1.00)
1.00
0.00
7.00

1.41 (1.00)
0.67
1.00
5.00

1.32 (1.00)
0.51
1.00
3.00

1.34 (1.00)
0.64
1.00
5.00

1.36 (1.00)
0.61
1.00
5.00

25825 (20000)
16854.45
5000
10000025258 (20000)
14605.83
6500
90000

26825 (20000)
18553.60
6000
11000025969 (20000)
16706.01
5000
110000

100

100

100

300

p-value

Preknowledge
Average (Median) a
SDb
Min
Max

0.32d

Age
Average (Median)
SD
Min
Max

0.44d

GenderAverage (Median)
SD
Min
Max

0.96e

Education
Average (Median)
SD
Min
Max

0.02e

Total family member under 18 (TFM)
Average (Median)
SD
Min
Max

1.15 (1.00)
0.96
0.00
5.00

0.76e

Total earning member (TEM)
Average (Median)
SD
Min
Max

0.87e

Household income c
Average (Median)
SD
Min
Max
Sample size

0.94d

a Median in parenthesesb SD stands for standard deviationc Household income in BDT
d Kruskal-Wallis test is applied to examine whether or not the frequencies of the variables are independentamong the treatment groups
e Chi-squared test is applied to examine whether or not the frequencies of the variables are independentamong the treatment groups

18

295

averages of the sociodemographic variables conditional on specific treatments are almost similar296

to the overall (unconditional) averages of taking the same variables. The preknowledges of house-297

hold heads about organic and nonorganic food are similar among the treatments, and the overall

298

average preknowledge is neither high nor low (4.07). The mean age of the household head in the299

three treatments does not vary, and the average age is approximately 37 years old. Considering

300

gender, 43 %, 42 %, and 41 % household heads are female in baseline, deliberation and FD, re-301

spectively. Regarding education, household heads in baseline and deliberation possess ten years of302

schooling as a median, while in FD, household heads usually receive twelve years of schooling as

303

a median. Table 2 also indicates that, on average, households in each treatment include one earning

304

member and one family member under the age of 18. The average monthly household income is305

approximately 26 000 BDT in the overall sample, which does not vary among the treatments. The

306

Chi-squared and Kruskal-Wallis tests indicate that most of the independent variables are not sig-

307

nificantly different among the treatments, implying that the random assignments of the treatments308

through sampling processes are effective enough as initially intended.

309

Table 3 presents the summary statistics of weekly organic vegetable consumptions (OVCs) and310

nonorganic vegetable consumptions (NVCs) in three rounds. In the pretreatment round 1, house-

311

holds in baseline, deliberation and FD weekly consume 0.88, 0.82 and 0.64 kg (7.53, 7.21 and312

7.31 kg) organic (nonorganic) vegetables, respectively, indicating almost the same amount of veg-

313

etable consumptions among the treatments. However, households in baseline, deliberation and FD

314

weekly consume organic (nonorganic) vegetables 1.42, 2.47 and 3.24 kg (6.73, 5.62 and 4.63 kg) in315

the posttreatment round 1 and 2.18, 3.69 and 4.21 kg (5.25, 3.85 and 3.23 kg) in the posttreatment316

round 2, respectively. This implies that households in deliberation and FD gradually increase (de-317crease) OVCs (NVCs) compared to baseline over the two posttreatment rounds and the degree of318

increase (decrease) is higher in FD. Overall, table 3 reveals that households in different treatments319

exhibit different OVCs (NVCs) during the posttreatment rounds compared to the pretreatment. It

320

also appears to be true qualitatively in figure 4. In addition, Mann-Whitney tests validate the sig-

321

nificant differences in the distributions between baseline and other treatments (deliberation and

19

20

0.88
0.00
1.87
0.00
9.00

0.82
0.00
1.59
0.00
7.25

0.64
0.00
1.34
0.00
7.80

100

Sample size

100

7.21
6.50
3.99
1.00
24.00

c SD stands for standard deviationb FD stands for future designa Weekly consumptions in kg

7.53
6.63
4.49
0.00
27.50

Average
Median
SD
Min
Max
100

7.31
6.50
3.69
2.00
20.50

Nonorganic vegetable consumptions (NVCs)a

Average
MedianSDc
Min
Max

Organic vegetable consumptions (OVCs)a

Pretreatment round 1
Baseline
Deliberation
FDb

100

6.73
6.50
3.10
0.50
20.50

1.42
0.50
1.94
0.00
8.00

100

5.62
5.50
3.10
0.00
15.00

2.47
1.50
2.99
0.00
20.00

100

4.63
4.13
3.09
0.00
14.00

3.24
2.50
2.87
0.00
13.00

Posttreatment round 1
Baseline
Deliberation
FD

100

5.25
5.00
3.39
0.00
28.00

2.18
1.50
2.17
0.00
9.5

100

3.85
3.25
2.43
0.00
11.00

3.69
3.00
3.25
0.00
23.00

100

3.23
2.50
2.43
0.00
17.00

4.21
3.75
2.69
0.00
18.00

Posttreatment round 2
Baseline
Deliberation
FD

Table 3: Summary statistics of the dependent variables

900

5.71
5.00
3.67
0.00
28.00

2.17
1.00
2.69
0.00
23.00

Overall322FD) at 1 % level.

323

Table 4 exhibits the difference-in-difference (DID) estimates, i.e., average treatment effects324

on the treated (ATT), based on equations (1) to (3), indicating the impact of the treatments. It

325

is apparent that DID estimates of deliberation and FD on OVCs show consistently positive and326

statistically significant results at 1 % level in models 1-1 and 1-2, respectively (table 4). However,327

DID estimates of FD on NVCs are statistically significant at 1 to 5 % level with a negative sign328

in model 2-2. Particularly in posttreatment round 1 that households receive deliberation and FD,

329

OVCs are estimated to increase by 1.12 and 1.92 kg, respectively and in posttreatment round 2,330

they are estimated to increase by 1.62 and 2.21 kg, respectively, as compared to baseline. The

331

aggregate coefficients reveal that over three rounds, households in deliberation and FD weekly

332

OVCs
consume organic vegetable on average ATTOVCsdeli (2, 3) = 1.37 and ATTFD (2, 3) = 2.07 kg more333

than baseline, respectively. Moreover, in posttreatment round 1 and posttreatment round 2, NVCs

334

are estimated to decrease by 1.78 and 1.65 kg for FD households as compared to baseline, respec-

335

tively. The aggregate coefficient shows that over three rounds, households in FD weekly consume336

nonorganic vegetable on average ATTNVCs
FD (2, 3) = 1.72 kg less than baseline. Overall, it can be

337

interpreted that FD induces a lasting shift from nonorganic to organic vegetable consumptions as

338

compared to baseline and it has a more economically significant impact, almost 2.0 times greater339

than deliberation. In addition, we also do the subsample analyses by considering deliberation as

340

a base group and obtain positive (negative) impacts of FD on OVCs (NVCs) which demonstrate a341

great potential of FD compared to any other treatments.3

342

Table 5 represents the marginal effects of the independent variables in two-part models to check343

the robustness of our results and the main results remain the same.4 We mainly center on reporting344

the marginal effects of treatment dummies, age, gender and household income, because they are3
In subsample analyses that include deliberation (base group) and FD treatments, DID estimates of FD on OVCs
(NVCs) show positive (negative) and statistically significant results at 10 % level in posttreatment round 1 and at 11
to 12 % level in aggregate, respectively (see the appendix D for the results of subsample analysis). These results
can be considered practically significant in view of the weekly organic and nonorganic vegetable consumptions ofBangladeshi households.
4
Because of having less number of zero observations (approximately 5 %), we apply glm (gamma-log) by using
equation (5) to identify the effect of independent variables on NVCs (Wooldridge, 2010, Smith et al., 2017).

21

(a) Households organic vegetable consumptions (OVCs) in three rounds(b) Households nonorganic vegetable consumptions (NVCs) in threeroundsFigure 4: Boxplots of organic and nonorganic vegetable consumptions22

23−1.18*−1.65**−1.78***

Posttreatment round 2
Coefficient−0.69

Posttreatment round 1
Coefficient

2.21***

1.92***
ATT on NVCsc

1.62***

Posttreatment round 2
Coefficient

1.12***

Posttreatment round 1
Coefficient

ATTaon OVCsb

c NVCs stands for weekly nonorganic vegetable consumptions in kg

b OVCs stands for weekly organic vegetable consumptions in kg

a ATT stands for average treatment effects on the treated***significant at the 1 percent level, **at the 5 percent level and *at the 10 percent level.

Treatments
Deliberation treatment vsbaseline treatment (model 2-1)
Future design treatment vsbaseline treatment (model 2-2)

Treatments
Deliberation treatment vsbaseline treatment (model 1-1)
Future design treatment vsbaseline treatment (model 1-2)

Difference-in-difference (DID) models−1.72***−0.93

Aggregate
Coefficient

2.07***

1.37***

Aggregate
Coefficient

600

600

600

600

Sample size

Table 4: Average treatment effect on organic (OVCs) and nonorganic (NVCs) vegetable consumptions

24
300

0.08
0.03**−0.41−5.12 × 10−06
0.09
0.22
0.22
300

−0.18
0.019
−0.59*
1.80×10−05 *
0.05
0.29
0.10

−1.08***−2.17***

Model 1-2 (glm)Model 1-1 (two part)

1.17***
1.74***

ME

300

−0.09
0.04***−0.31−9.57 × 10−06
0.25**
0.37**
0.09

1.38***
1.86***

Model 2-1 (two part)ME (combined)

OVCs

300

0.03
−0.01
−0.78**
1.74×10−05 *−0.19*
0.03
0.16−1.24***−1.83***

Model 2-2 (glm)

ME

NVCs

Posttreatment round 2

900

0.09*
0.03***−0.26−4.78 × 10−06
0.08
0.28***
0.11

1.93***
2.41***

Model 3-1 (two part)ME (combined)

OVCs

Panel

900

−0.18*
0.007−0.63**
3.1×10−05 ***
−0.06
0.14
0.007

−2.05***−3.06***

Model 3-2 (glm)

ME

NVCs

***significant at the 1 percent level, **at the 5 percent level and *at the 10 percent levela OVCs stands for weekly organic vegetable consumptions in kg
b NVCs stands for weekly nonorganic vegetable consumptions kg
c ME stands for a marginal effect to indicate a change in the weekly organic (or nonorganic) vegetable consumptions when one independent variable increases by one unit, holding
other factors fixed

Sample size

Treatment dummies(Base group = Baseline treatment)
Deliberation treatment
Future design treatment
Control factors
Knowledge
Age
Gender (Base group = Male)Household income
Education
Total family members under 18
Total earning member

NVCsb

OVCsa
MEc(combined)

Posttreatment round 1

Table 5: Marginal effects of the independent variables on household’s organic and nonorganic vegetable consumptions

345

identified to remain significant at 1 to 10 % in posttreatment round 1, posttreatment round 2 and346

panel. In models 1-1, 2-1 and 3-1, the age is identified to be consistent and statistically significant

347

with a positive sign on OVCs, meaning that an additional year increase of household head’s age

348

is associated with an increase in OVCs. In models 1-2, 2-2 and 3-2, regarding the gender dummy,349

females are interpreted to be consistently significant with a negative sign and household income

350

is identified to be statistically significant (having a very small magnitude) with positive signs for351

NVCs. This means that women are generally more negative towards nonorganic vegetable con-352

sumptions than men. In posttreatment round 1 that households receive deliberation and FD, OVCs

353

(NVCs) are estimated to increase (decrease) by 1.17 and 1.74 kg (1.08 and 2.17 kg), respectively

354

and in posttreatment round 2, they are estimated to increase (decrease) by 1.38 and 1.86 kg (1.24355

and 1.83 kg), respectively, as compared to baseline. In the panel regression, it is evident that356

deliberation and FD receiving households weekly consume 1.93 and 2.41 kg (2.05 and 3.06 kg)

357

more (less) organic (nonorganic) vegetable than baseline, respectively. It can be concluded that358

FD results in a sustained transition from nonorganic to organic vegetable consumptions relative to359

baseline, and it has a nearly 1.5-times greater economic impact than deliberation. Furthermore,360

we conduct subsample analyses that take into account deliberation as a base group and FD treat-361

ments.5 These analyses also reveal that FD has positive (negative) impacts on OVCs (NVCs) which362

highlights its significant potential compared to any other treatments.

363

Overall, it is evident from the summary statistics that the random assignments of the treat-364

ments through sampling processes are effective enough (table 2) and households vegetable con-365

sumptions (organic and nonorganic) get different as time goes from pretreatment round to post-

366

treatment rounds (table 3). We quantify the difference and check its robustness across the treat-367

ments through DID and two-part models, respectively. As indicated in our conceptual framework,

368

household OVCs and NVCs are impacted by FD over the time periods as compared to those in369

baseline and deliberation in practically and statistically significant manners (figure 3). In sum5

In subsample analyses that include deliberation (base group) and FD treatments, the marginal effects of FD on
OVCs (NVCs) show positive (negative) and statistically significant results at 5 % level in posttreatment round 1 and inpanel, respectively (see the appendix D for the results of subsample analysis).

25

370

mary, the average treatment effects across different lengths of treatment exposure for OVCs are

371

OVCs
OVCs
OVCs
ATTOVCsdeli (2, 2) = 1.12, ATTdeli (2, 3) = 1.37, ATTFD (2, 2) = 1.92 and ATTFD (2, 3) = 2.07,

372

NVCs
= −1.72 (table 4). The estimatedwhile those for NVCs are ATTNVCs
FD (2, 2) = −1.78 and ATTFD

373

average effects indicate that over three rounds, households in FD weekly consume organic (nonor-

374

ganic) vegetable on average 2.07 kg (1.72 kg) more (less) than baseline. FD results in a sustained

375transition from nonorganic to organic vegetable consumptions and the effect under FD is approx-

376

imately twice as much as that under deliberation in magnitude. These are robust and consistent377

with the results obtained from two-part panel regressions. The estimation results associated with

378

OVCs and NVCs provide answers to our research questions (how does the FD approach impact

379

food consumption?) and support the alternative hypothesis (FD induces a lasting shift to SFC)

380

by rejecting the null. Finally, our research establishes that FD demonstrates a great potential for381

inducing people to make a persistent change to sustainable food consumption (SFC).

382

Capitalism emerged as a socioeconomic system 300 - 400 years ago where private agents have383

ownership over properties based on their own interests, and transactions of commodities and ser-384

vices are made through demands and supplies in markets along the prices (Jahan and Mahmud,

385

2015, Brayshay, 2020). The goal is claimed to be benefiting a society as a whole by allowing

386

decentralized market mechanisms for consumptions, productions and the exchanges (Harris and

387

Delanty, 2023). The fundamental characteristic of such capitalism is a strong drive for private

388

agents to make a continuous differentiation towards profits. In old agrarian societies, a geograph-389

ical difference had generated profits, when people transported some commodities and resources

390

form one place to the other (Brayshay, 2020). In industrial societies, a production-process dif-391

ference has generated profits, enabling to make mass productions and consumptions with cheap

392

costs all over the world (Iwai, 1992). In post-industrial societies, an informational difference is393

now expected to generate profits, when private agents can edit and convey the information asso-394

ciated with commodities and services as a story (or a narrative) to people by utilizing the advent395

of digital technologies and platforms (Gilliam and Flaherty, 2015, Iwai, 2015, Shiller, 2019, 2020,

396

Li et al., 2019, Kemp et al., 2021, Júnior et al., 2023). It is also claimed that long-run behav-

26

397

iors get impacted, when people receive some information and find the meaning and significance398

as a story through their deliberative cognitive processes, such as psychological ownership (Evans,

399

2008, Frankl, 2008, Kahneman, 2011, Rosa et al., 2021). In this sense, a set of interventions in-400

troduced by FD is considered one effective approach that induces people to endogenously trigger

401

their deliberative cognitive processes, edit and interpret information as their long-run life story.

402

Therefore, it shall be possible to argue that households in our experiments have made a persistent403

change to SFC, voluntarily finding the meaning and significance by FD.

404

4Conclusion

405This paper has examined the effect of the future design (FD) approach on sustainable food406

consumption (SFC), investigating the question “how does the FD approach impact food consump-

407

tion?” and the hypothesis “FD induces a lasting shift to SFC.” To this end, we have implemented a408

3-round social experiment with three treatments of “baseline,” “deliberation” and “FD,” for collect-409

ing data on organic vegetable consumptions (OVCs), nonorganic vegetable consumptions (NVCs)

410

and sociodemographic factors of 300 households in Bangladesh over three months. The results411

show that FD induces people to have a sustained increase in organic and decrease in nonorganic

412

vegetable consumption as compared to any other treatment, and the effect under FD is approx-

413

imately twice as much as that under deliberation in magnitude and in each round. Overall, FD414

demonstrates a great potential for inducing people to make a persistent change to SFC. The novel415

aspects of this study are (i) to consider the perspective taking of future generations for analyzing

416

households lasting shifts to SFC by conducting multiple rounds of social experiments and (ii) to417

empirically identify real choices and consumptions of households between organic and nonorganic

418

vegetables instead of declared intentions.

419

We acknowledge certain limitations in our study and provide potential avenues for future re-420

search. First, this research focuses on organic and nonorganic vegetable consumptions by house-

421

holds to examine the sustained impact of FD on SFC, which is considered the best approximation

27

422

we can make in the context of Bangladesh. However, future studies should be able to collect and423

use household total food consumption and food waste data to identify the detailed results. Sec-424

ond, our study does not analyze the intricate mechanisms of how and why FD affects households

425

motivations, decisions and behaviors on SFC. With an additional experimental design or further ex-426

periments using the neuropsychological approach and qualitative interviews, future studies should427

be able to characterize how and why FD households change their behaviors (Shahen et al., 2021).

428

Third, in order to extend the applicability of our research findings, future studies should employ

429

the FD approach to address several other sustainability problems. While we recognize that our re-430

search may have other limitations, we firmly believe that it represents an advancement in promoting

431

a persistent change to SFC, and further studies on this topic will help solidify these findings.

432

5

Appendix433

We provide (A) the experimental instructions for an overview of the programs, (B) a document434

for collecting the information of pretreatment knowledge and (C) the case-method materials on435

the history and current situation of organic and nonorganic foods to the Bangladeshi households436

in the field as materials of the appendices. We also prepare appendix (D), the tables of subsample

437

analyses by considering deliberation (base group) and future design treatments, to support the438robustness of our results.

6

Acknowledgments

We would like to thank the anonymous referees, Ryohei Hayashi, Yuta Yasui, Husniddin
Sharofiddinov, Md. Tawhidul Islam, Pradip Hajong, Abdullah Al Zabir and Dr. Nazim Uddin for
their helpful comments, advice and supports. The authors specially thank the Regional Agricultural
Research Station, BARI, Jashore, Bangladesh Agricultural Development Corporation, Jashore andDepartment of Agricultural Extension (Jhenaidah and Jashore) under the Government of the People’s Republic of Bangladesh for their support. We are also grateful to the financial support fromthe Japanese Society of the Promotion of the Science as the Grant-in-Aid for Scientific Research
B (19H01485), Research Institute for Future Design and Kochi University of Technology.

28

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