01 Motivations and theoretical background

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01/07/2019

THE EFFECTIVENESS OF A STORAGE MANAGEMENT INTERVENTION ON

FOOD WASTE

By

GUIGUES Paonia S3572811

Impact of Ovie Smarterware on the consumption of leftovers and the moderating roles of risk aversiveness and hedonic values

A quantitative research

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Agenda Style Table of contents

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01

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04 01 Motivations and theoretical background

02 Methodology

03 Results & Discussion

04 Implications

05 Limitations & future research

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MoOvaOons & literature

background

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What & Why

• Worldwide: 30% of produced food lost or wasted (FAO, 2011)

• In household, around 60% of food waste is avoidable (WRAP, 2017)

• Households contribute the most to food waste within the European Union at 53% (FUISIONS, 2016)

Food waste is an important issue

• Suboptimal storage (Quested et al., 2011)

• Missunderstanding of use-by and best-before date labels(Graham-Rowe, et al. 2014)

• Lack of knowledge regardingg food edibility(Evans, 2011; Tsiros & Heilman, 2005)

• Leftovers are one of the most wasted food categories(Koivupuro et al., 2012; Quested et al., 2013)

Storage is a critical management phase

• Personal, health and financial attitudes are predictor of peoples’ food waste behaviours(Visschers et al., 2016)

Studies provide liQle guidance about peoples’ aStudes towards food waste

• Food storage interventions are not sufficiently explored (Hebrok, & Casper. 2017)

• Lack of research on the impact of technological tools tackling waste reduction(Schanes et al. 2018)

• What about intervention regarding leftovers ?

Research on storage interventions effectiveness are limited

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Ovie Smarterware (video)

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Research questions

“ Does a storage management intervention reduce food waste? ”

1.

To what degree does consumers’ risk aversion impact the eﬀecFveness of an intervenFon in the storage phase?

2.

To what extent can consumers’ hedonic values inﬂuence the eﬀecFveness of an intervenFon in the storage phase?

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Hypotheses development

01 The storage management intervention increases the likelihood of consuming leftovers.

03 The storage management intervention leads to a weaker increase in leftovers consumption, the higher the hedonic values are.

02 The storage management interven>on leads to a stronger increase in lecover consump>on the higher the risk aversiveness is.

• Visual cues, such as colours, play an essential role in assessing the quality of a product (ST Wang, 2013)

• As consumers’ ability to make a conscious decision is limited, people rely on heuristics (Szmigin & Piacentini, 2014)

• Individuals make a direct connection between green and positive attributes e.g. safety (Elliot et al. 2009)

• Conflictual relationship between avoiding food wastage and protecting oneself against these risks (Evans, 2011)

• Risk aversion impacts consumers’ decision making and relates to uncertainty (Shimp & Bearden, 1982; Qualls & Puto, 1989)

• Highly risk averse people will trust any sources of information e.g. green light = safe for consumption (Dosman et al., 2001)

• Aim of intervention, pro-environmental behaviours are not in line with hedonic values (Batra & Ahtola, 1991Steg et al., 2014)

• Hedonic values acts as a barrier to behavioural change of pro-environmental actions (Steg et al., 2014)

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Conceptual model

Storage management intervention

(Ovie Smarterware)

H1 + H2 +

H3 - Consumers’ risk

aversiveness

Consumers’

hedonic values

Likelihood of

consuming leftovers

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Methodology

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Experiment set-up

Research design and data collection

• Quantitative research

• Online questionnaire

• Experimental between-subject design:

Measurements

• IV: Behavioural intention (Hutchinson et al., 2009)

o 3 items o 1 to 7 scale

• DV: Likelihood of consuming leftovers

o No intervention or Intervention scenario o Randomly assigned

• Moderator 1: Risk aversiveness (Mandrik & Bao, 2005)

o 6 items o 1 to 7 scale

• Moderator 2: Hedonic values (Schwartz, 1992)

o 3 items

o -1 to 7 scale No intervention (control group) vs. Intervention (study group)

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Results & Discussion

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Sample characteristics

Sample

• 97 participants per group

• Mostly females

• Average age 29 years old

• More than a half are students

• Most household did not have a child

• Well distributed household size

• High income

• 81.4% of people have a university degree or higher

Variables

• Average inten>onal behaviour: 5.473

• Average risk aversiveness: 4.337

• Average hedonic values: 6.280

28,40%

25,80%

14,90%

22,70%

8,20%

39,70%

60,30%

Male Female

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The storage management intervention does not increase the likelihood to consume leftovers

Hypothesis 1 - rejected

Contextual or psychological factors could also explain a persons’ behaviour

Role of the used scenarios

3 3,5 4 4,5 5 5,5 6

No intervention Intervention 6

5.5

5

4.5

4

3.5

3

Mean of intentional behaviour to consume salad

leftovers Personal preferences should be considered

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Hypothesis 2 - supported

The storage management intervention leads to a stronger increase of leftovers consumption, the higher the risk aversiveness is

Trust in sources of information is linked to food safety issues (Questionnaire)

People are following the guidelines

Does people saying match its behaviour?

Do they fully trust the new technological tool?

?

4,8 5 5,2 5,4 5,6 5,8 6

- 1 SD = 3.076 Mean = 4.277 + 1 SD = 5.479 Intervention No intervention

Mean plot intervention on the likelihood of leftover consumption, depending on the moderation of risk aversiveness

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Hypothesis 3 - rejected

The storage management intervention does not lead to a weaker increase of leftovers consumption, the higher the hedonic values are

Doing the right thing makes people feel good about themselves

2 effects cancelling each other out: Convenience of leftovers vs. Challenging tool to use

Restricted variety of food options

Model Beta

coeff.

Std. Error t p LLCI ULCI

(Constant) 2.326 1.459 1.595 .113 -.560 5.212

Ovie

Smarterware (IV)

-2.079 1.338 -1.553 .123 4.727 .569

Gender .563 .256 2.200 .030 .057 1.069

Age -.017 .012 -1.416 .159 -.040 .007

Education .663 .243 2.730 .007 .183 1.143

Household size -.266 .126 -2.109 .037 -.516 -.017

Number of children

.410 .267 1.534 .127 -.119 .938

Household net income

-.019 .072 -.263 .793 -.160 .123

Hedonic values .198 .142 1.395 .165 -.083 .480

Interaction effect .301 .211 1.424 .157 -.117 .718

DV: Intentional behaviour

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Implications

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Implications & contributions

Hypotheses Confirmed

H1: The storage management intervention increases the likelihood of consuming leftovers.

H2: The storage management intervention leads to a stronger increase in the consumption of leftovers, the higher the risk aversiveness is.

H3: The storage management intervention leads to a smaller increase in the consumption of leftovers, the higher the hedonic values are.

Give insights of consumers ’ food waste behaviour

Consumers need to be educated about the perishability of food product

Risk aversiveness should be regarded when investigating food waste and can be used in campaigns development

Focus on the feeling good and novelty seeking aspect to outweigh the usage difficulty

Companies should continue to create and make the usage of food storage interventions easier

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Limitations & future research

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Research Limitations and future reseach

1. Sample problems

2. Data collection limits: measurements and used method

3. Restricted choice of meal

4. Limitation of PROCESS for SPSS regarding risk aversiveness results

5. Focus on one feature of the intervention tool

Get a more representative sample & incorporate psychological and contextual factors

Extend the observation time & integrate the intervention tool in an everday-life set-up Use different categories of food

Test results with another software

Incorporate the other features of Ovie Smarterware

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Thank you for your attention

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References

• Batra, R., & Ahtola, O. (1991). Measuring the hedonic and utilitarian sources of consumer attitudes. Marketing Letters, 159-170.

• Dosman, D. M., Adamowicz, W. L., & Hrudey, S. E. (2001). Socioeconomic determinants of health-and food safety-related risk perceptions. Risk analysis, 21(2), 307-318.

• Elliot, Andrew J., Markus A. Maier, Martin J. Binser, Ron Friedman, and Reinhard Pekrun (2009), “The Effect of Red on Avoidance Behavior in Achievement Contexts,” Personality and Social Psychology Bulletin, 35 (3), 365-75.

• Evans, D. (2011). Blaming the Consumer – Once Again: The Social and Material Contexts of Everyday Food Waste Practices in Some English Households. Critical Public Health, 21(4), 429–440.

• FAO. (2011). Global food losses and food waste – Extent, causes and prevention. Retrieved June 14, 2019, from http://www.fao.org/3/mb060e/mb060e.pdf

• FUSIONS. (2016). Estimates of European food waste levels. Retrieved June 14, 2019, from https://urlz.fr/9itd

• Graham-Rowe, E., Jessop, D., & Sparks, P. (2014). Identifying motivations and barriers to minimising household food waste. Resources, Conservations and Recycling, 84, 15-23.

• Hebrok, M., & Bosk, C. (2017). Household food waste: drivers and potential intervention points for design – an extensive review. Journal of Cleaner Production, 151, 380-392.

• Hutchinson, J., Lai, F., & Wang, Y. (2009). Understanding the relationships of quality, value, equity, satisfaction, and behavioral intentions among golf travelers. T ourism management, 30(2), 298-308.

• Koivupuro, H.-K., Hartikainen, H., Silvennoinen, K., Katajajuuri, J.-M., Heikintalo, N., Kok, G., Schaalma, H., Ruiter, R. A., Van Empelen, P.,

& Brug, J. (2004). Intervention mapping: protocol for applying health psychology theory to prevention programmes. Journal of health psychology, 9(1), 85-98.

• Mandrik, C. A., & Bao, Y. (2005). Exploring the concept and measurement of general risk aversion. Advances in Consumer Research, 32, 531-539.

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References

• Qualls, W. J., & Puto, C. P. (1989). Organiza>onal Climate and Decision Framing An Integrated Approach to Analyzing Industrial Buying Decisions. Journal of MarkeFng Research, 26(2), 179-192.

• Quested, T., Mars, E., Stunell, D., & Parry, A. (2013). Spagheq soup: The complex world of food waste behaviours. Resources, ConservaFon and Recycling, 79, 43-51.

• Quested, T., Parry, A. D., Easteal, S., & Swannell, R. (2011). Food and drink waste from households in the UK. NutriFon BulleFn, 36(4), 460-467.

• Schanes, K., Dobernig, K., & Gözet, B. (2018). Food waste masers-A systema>c review of household food waste prac>ces and their policy implica>ons. Journal of Cleaner ProducFon, 182, 978-991.

• Schwartz, S. H. 1992. Universals in the content and structure of values: Theore>cal advances and empirical tests in 20 countries.

Advances in Experimental Social Psychology, 25, 1-65.

• Shimp, T. A., & Bearden, W. O. (1982). Warranty and other extrinsic cue eﬀects on consumers' risk percep>ons. Journal of Consumer research, 9(1), 38-46.

• ST Wang, E. (2013). The inﬂuence of visual packaging design on perceived food product quality, value, and brand preference.

InternaFonal Journal of Retail & DistribuFon Management, 41(10), 805-816.

• Steg, L., Perlaviciute, G., van der Werﬀ, E., & Lurvink, J. (2014). The Signiﬁcance of Hedonic Values for Environmentally Relevant Aqtudes, Preferences, and Ac>on. Environment and Behavior, 46(2), 163–192.

• Szmigin, I & Piacen>ni, M. (2018). Consumer Behavior. (2nd ed.). Oxford: Oxford University Press.

• Tsiros, M., & Heilman, C. (2005). he Eﬀect of Expira>on Dates on the Purchasing Behaviour for Grocery Store Perishables. Journal of MarkeFng, 69(2), 114–129.

• Visschers, V. H., Wickli, N., & Siegrist, M. (2016). Sor>ng out food waste behaviour: A survey on the mo>vators and barriers of self-

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GUIGUES Paonia S3572811

01 Motivations and theoretical background

Agenda Style Table of contents

Contents

01

04