Consumption of low pesticides food: implications for producers and policymakers. Results from a multi-attribute analysis

Paola Ferretti (Department of Economics, Ca' Foscari University of Venice, Venice, Italy)
Aiste Petkeviciute (Faculté des sciences économiques sociales politiques et de communication, Louvain Institute of Data Analysis and Modeling in Economics and Statistics, Université catholique de Louvain, Louvain-la-Neuve, Belgium)
Maria Bruna Zolin (Department of Economics, Ca' Foscari University of Venice, Venice, Italy)

British Food Journal

ISSN: 0007-070X

Article publication date: 14 March 2023

Issue publication date: 18 December 2023

770

Abstract

Purpose

This study aims to identify different consumer segments to address the strategies that can be adopted by companies and policymakers to increase the consumption of safer foods and reduce the negative externalities caused by pesticides. More than 3,000 consumers were involved in the survey, of which more than 1,000 completed in all parts.

Design/methodology/approach

The complexity of the topic required a multidimensional approach. Therefore, the authors modelled the decision support system by proposing a decision rule-based approach to analyse consumers' food purchasing choices. More precisely, the authors referred to the dominance-based rough set approach (DRSA).

Findings

Based on the DRSA results, three consumer segments were identified: green consumers, integrated pest management (IPM)-informed and active consumers, and potential low-pesticide consumers for which different policy implications have been highlighted.

Research limitations/implications

Despite the high number of survey respondents, further research should seek to obtain data from a more balanced sample. Furthermore, different methods of analysis could be applied and the results compared.

Practical implications

Identification and promotion of managerial and public policies to increase the consumption of low pesticide food.

Social implications

The main social implications can be summarised in the greater knowledge and awareness of the environmental aspects related to food, recognition of the intrinsic quality and/or functionality of food.

Originality/value

The authors contribute to the literature in two ways. First, the authors refer to the DRSA, an innovative approach in the context of consumer analysis. Second, based on the decision rules, the authors identify three consumer segments to which specific tools can be addressed.

Keywords

Citation

Ferretti, P., Petkeviciute, A. and Zolin, M.B. (2023), "Consumption of low pesticides food: implications for producers and policymakers. Results from a multi-attribute analysis", British Food Journal, Vol. 125 No. 13, pp. 277-295. https://doi.org/10.1108/BFJ-03-2022-0222

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Paola Ferretti, Aiste Petkeviciute and Maria Bruna Zolin

License

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode


1. Introduction

The adoption of sustainable farming systems positively affects the environment and health of farmers and consumers (Carvalho, 2006; Defrancesco et al., 2008; Pimentel and Burgess, 2014; Lazzarini et al., 2018; Petrescu et al., 2019). The integrated pest management (IPM) method dates back to the 70s. Integrated pest management involves the production of healthy crops using growing methods that disturb rural ecosystems as little as possible. This limits the use of phytosanitary products (fungicides, herbicides, insecticides, etc.) and promotes natural systems and methods of phytosanitary control (Van Lenteren, 1997; Kogan, 1998; FAO, 2006; Lamine, 2011; Puente et al., 2011; Peterson et al., 2018; Midingoyi et al., 2019). Integrated pest management (Kogan, 1998) prevents pest infestations by adopting sustainable agricultural practices, such as the rotation and selection of seeds that are more resistant to pest attacks. This method requires a monitoring system and setting of thresholds that help the farmer decide when pest control is needed. It has been mandatory in the European Union since 2014 because of Directive 2009/128/EU of the European Parliament and Council concerning the placement of plant protection products (PPP) on the market and repealing Council Directives 79/117/EEC and 91/414/EEC (Barzman and Dachbrodt-Saaydeh, 2011). As the European Commission (2017) underlines, the implementation of Directive 2009/128/EC, which aims to reduce the risk and use of pesticides, is still insufficient to achieve environmental and health improvements. According to the European Court of Auditors (2020), despite the mandatory directive at the European level, there has been little reduction in the risks derived from PPP use. Farmers are obliged to adopt IPM methods, but they are not required to record how the methods are carried out. Furthermore, the adoption of IPM is not a necessary requirement to receive payments from the European Union. Because of the strong impact of the agri-food chain on the environment (European Commission, 2020), a targeted strategy for the food system known as “Farm to Fork” was introduced in May 2020 as part of the European Green Deal, with the specific aim of making the European food system. One of the specific objectives of “Farm to Fork” is a 50% reduction in the use of chemical pesticides by 2030 and a revision of the Directive on the sustainable use of pesticides, adopted in 2009 and criticised for poor implementation in most member states, is in progress.

The need for consumer involvement in the green transition is widely recognised.

Consumers, as well as producers, are placed at the centre of the new EU Agenda. Consumer behaviour and innovation should play a crucial role in the path to a wider sustainable food system. Environmentally and health-conscious consumers can encourage producers to increase and spread sustainable production, but they are often unaware of the production techniques adopted and the extent to which the techniques may or may not be beneficial to their health. Previous studies analysing consumer behaviour and/or attitudes towards sustainable food consumption (Rimal et al., 2001; Padel and Foser, 2005; Vermeir and Verbeke, 2006; Gotschi et al., 2010; Grunert et al., 2014; Johe and Bhullar, 2016; Žibret and Kline, 2016; Lewis et al., 2017; Kautish et al., 2019; Mastronardi et al., 2019; Savelli et al., 2019) have been primarily survey-based and have predominantly focused on organic products (Aertsens et al., 2009; Kautish et al., 2022).

Based on a survey of 1,103 households in different countries, mainly located in Europe, this study focused on consumers' choices regarding the purchase of IPM food products with a lower quantity of pesticides, a topic on which there is little research (Govindasamy and Italia, 1998; Stranieri et al., 2017; Canavari et al., 2018; Mazzarolo et al., 2020). Current and potential food purchasing choices are analysed as a whole, without detailed distinctions between behaviour, attitudes and perceptions.

This study aims to identify different consumer segments to which address strategies that can be adopted by companies and policymakers to increase the consumption of safer foods and to reduce the negative externalities caused by pesticides.

The complexity of the topic requires a multidimensional approach. Therefore, we propose a decision rule-based approach to analyse consumers' food purchasing choices. More precisely, we refer to the dominance-based rough set approach (DRSA), an innovative approach in the context of consumer analysis (Roma et al., 2020). It can be used to analyse inaccurate and vague descriptions of objects, conduct an in-depth exploration of the data, evaluate the informative content of the attributes under examination and develop decision rules that can support the evaluation process (Greco et al., 2001, 2002).

The paper is organised as follows. Section 2 presents the data, proposed methodology and research design. Section 3 discusses the data sample. Section 4 presents the results obtained with DRSA. Section 5 discusses the main results, and Section 6 concludes the paper.

2. Data and methods

2.1 Data

Data was collected using a web-based survey tool via an online questionnaire (Qualtrics, Provo, UT, USA). The structure of the questionnaire follows that prepared by a previous research of a Horizon project (H2020, EUCLID, 2015–2018) in which experts belonging to naturalistic and economic disciplines took part. They were asked to review the questionnaire and improve its reliability and validity. The questionnaire consists of 35 questions, each of which represents an indicator. The study was conducted over an 8-week period (1 March 2021 to 30 April 2021). Given the specificity of the topic, a virtual snowball sampling technique was used (Goodman, 1961). Using the authors' networks, the interviewees were asked to forward the survey link to family, friends, colleagues who they believe involved in food purchase choices. More than 3,000 consumers were involved in the survey, of which 1,103 were completed in all parts. The completion rate is approximately 37%. The first note concerns the greater participation of women and the higher educational qualifications of the interviewees, as if to indicate greater sensitivity to the issues of sustainable food consumption of women and people with higher educational qualifications.

The 35 questions (attributes) of the questionnaire were partitioned into three blocks, Bi (i = 1,2,3):

  1. Basic characteristics (B1): These questions were used to collect data on age, gender, education level, income range, place of residence, nationality, number of people in the family and profession (9 questions).

  2. Eating and grocery shopping habits (B2): These questions were used to collect data on participants' diets, what part of their diets consisted of horticultural products, whether the decision maker was responsible for food choices in her/his household, where food was regularly purchased and what factors determined the choice (7 questions).

  3. Perception of and attitudes toward IPM products (B3): These questions were used to assess the familiarity of participants with the IPM method and their knowledge of differences between IPM and organic agriculture, and to determine whether they could distinguish the IPM method from others commonly discussed in the media, such as genetically modified organisms (GMOs) and post-harvest measures (19 questions).

2.2 Method: DRSA

To provide suggestions that can support policymakers, farmers or companies, it is necessary to produce informative and easy-to-understand results, in which evaluation paths are transparent and based on indicators that can be updated; thus, the model should be dynamic and, for this reason, suitable for use as a continuous evaluation tool.

The method we propose consists of the following steps:

  1. Definition of the information system IS=(U,A), where U is the (Universe) set of all 1,103 survey participants and A=i=13Bi is the set of all information gathered in the questionnaire.

  2. The definition of the disjoint sets of the condition and decision attributes C and D, respectively, such that the set of all information A is partitioned as A=CD. The choice of considering a question as a condition attribute (i.e. an attribute characterising the sample) or as a decision attribute (i.e. information marking the category of the sample) can change during the analysis. This is due to two reasons: (1) because our aim was to develop a general framework for the analysis of consumer food choices, it was necessary to consider different interrelations between the collected information. (2) Multiple answers and nested questions were presented in the questionnaire.

  3. Definition of the coefficient matrix (different choices of the partitioning condition C and decision D attributes give origin to different coefficient matrices) with reference to the particular partition of A under consideration: f(xi,aj)Vaj denotes the answer given by i-consumer (i=1,,1,103) with reference to j-question. The set Vaj is called the domain set and contains all the values (i.e. given answers) assumed by attribute (i.e. question) aj; in some cases, a fixed-point scale answer was attached to the question.

  4. Extraction of DRSA decision rules: in the form of if then sentences, the conditions that characterise the rule and the resulting decision class assignment can be explicitly displayed.

  5. Sensitivity analysis and suggestions: by explicitly representing the dependence between condition and decision criteria (conditions → decision), decision rules provide exhaustive and easy descriptions of patterns in the data and, therefore, are the best means to analyse the results and to communicate them to the operators.

In choosing the framework to model our decision support system, the possibility of considering both numerical and categorical data, the non-use of statistical assumptions on the distribution of data and the absence of the need for structures that collect data (e.g. functions or equations) were all factors that guided our analysis. We were also interested in a simple method for the description of schemes that were exhibited by the data, using a useful and flexible tool that can be easily updated and is capable of capturing the fundamental characteristics of the information system in a multifaceted representation (see case studies conducted by Celotto et al., 2015; Zolin et al., 2017; Ferretti et al., 2020; Roma et al., 2020).

The key idea of a universe U partitioned into equivalence classes is not sufficient when objects are described by attributes with domains that are preference orders, given that inconsistencies can be generated by violations of the dominance principle (Greco et al., 2001, 2002). However, in the analysis of multi-criteria decisions, it is possible that some of the attribute domains are ordered; therefore, it is necessary to explicitly consider preference relations in attribute domains.

In DRSA, the main assumption is that each domain Vaj is completely preordered by an outranking relation ai with the following meaning: xajy when x is at least as good as y with respect to criterion aj. If each domain Vaj is real valued, then it is xajy if and only if f(x,aj)f(y,aj).

In the case of decision criterion d, each element in U is assigned one class Clt (tN) such that the classes are preference-ordered, that is, when r>s, each element in Clr is preferred to each element in Cls. Suppose that m denotes the finite number of decision classes. Preferences can be either strict or weak. More precisely, if r>s and it is xϵClr and yϵCls, then x is at least as good as y’ and not y is at least as good as x’ with respect to the decision criterion. Equivalently, given the decision criterion d, under the same assumptions, it is f(x,d)f(y,d) while is not f(y,d)f(x,d).

Related to the previous assumption are the definitions of the upward and downward unions of classes Cls: Clt=stCls; Clt=stCls. Therefore, xϵClt implies that x belongs to at least class Clt, while xϵClt means that x belongs to at most class Clt.

With reference to condition criteria in C, it is possible to define a partial preordering DP (i.e. reflexive and transitive) for each condition criterion in PC, with the following meaning: xDPy if xaiy for each criterion aiP. In this manner, each element x in U is related to two sets: the P-dominating set and the P-dominated set, respectively

DP+(x)={yU:yDPx};DP(x)={yU:xDPy}.

If x dominates y on all condition criteria in PC, it also dominates y on the decision (i.e. element x should be assigned to at least as good a decision class as y). In our study, decision makers satisfying the dominance principle were consistent, while those violating the dominance principle were classified as inconsistent.

The P-dominating and P-dominated sets represent the basis of knowledge, in fact, knowledge is approximated by the upward and downward unions of decision classes; the P-lower approximation of Clt with respect to PC is P (Clt)={xU:DP+(x)Clt}, while the P-upper approximation of Clt with respect to PC is P¯ (Clt)={xU:DP(x)Clt}.

Analogous are the definitions for the P-lower and P-upper approximations of Clt with respect to PC:

P(Clt)={xU:DP(x)Clt}andP¯(Clt)={xU:DP+(x)Clt}.

Accordingly, the lower approximations are composed of elements belonging to the upward and downward unions of classes, whereas the upper approximations contain elements that can belong to the upward and downward unions of classes.

Finally, the upward and downward unions of classes are related to the upward and downward unions of the decision classes by the set inclusion relations:

P(Clt)CltP¯(Clt)andP(Clt)CltP¯(Clt)
and on these inclusion properties are based the definitions of the P-boundaries of Clt and of Clt:
BnP(Clt)=P¯(Clt)P(Clt)BnP(Clt)=P¯(Clt)P(Clt).

The related decision rules, that is, logical statements given by a composed condition component (if …) and a decision component (then …), in DRSA are formalised as D–decision rules, D–decision rules and D–decision rules, given that the information system can be described by decision rules based on dominance relations and associated approximations, and the related rules can be certain, possible or approximate. Certain rules follow from lower approximations, possible rules are linked to upper approximations and the approximate rules refer to the boundary regions.

Step 4 was performed using the VC-DomLEM algorithm implemented with the jMAF software (Błaszczyński et al., 2013) developed by the Laboratory of Intelligent Decision Support Systems at the Poznan University of Technology (http://wwwidss.cs.put.poznan.pl).

3. The sample in a nutshell

To describe our sample, we used simple descriptive statistics. Table 1 presents the basic characteristics of the respondents, including age, gender, education, profession and monthly income per person by geographic group. The majority of respondents were from Northern European countries.

Young women with a higher education and medium monthly income per person made up the largest part of the sample.

Table 2 reveals that more organic products were purchased than those obtained through the IPM methods. This finding suggests a lack of knowledge among the consumers. On average, only 11.6% of the respondents had purchased IPM products in the past six months.

Among all the respondents, 64.4% had purchased organic products within the past six months, whereas only 11.6% had purchased IPM products (Table 2). Table 3 presents data on the reasons why consumers have purchased, or would purchase, IPM products. Organic products were slightly more popular in Northern Europe and other countries, whereas more Southern Europeans had purchased an IPM product within the past six months, compared with the other groups.

As indicated in Table 3, the primary reason among consumers for having purchased or wanting to purchase IPM products was that they considered IPM products to be healthier. This was followed by ethical concerns and better product quality. There were significant differences between the geographical areas.

Moreover, the results of the survey provided evidence of vagueness in the term “quality’. Nevertheless, the highest percentage of respondents (50.4%) considered food grown with less pesticide to be of high quality, even though the low percentage of consumers (6.2%) who considered IPM products to be high-quality products is worthy of attention. This indicates a strong gap in consumer awareness of the terminology used to describe food-growing methods.

4. Results obtained with the DRSA

All 35 questions (Q) on the questionnaire represented a starting point for defining the condition attributes. The presence of multiple answers and nested questions implies that scores for these attributes can be higher than those obtained on the original questions.

The selected extracted decision attributes can be considered to model and describe consumer behaviour and consumer attitudes [1] as follows:

  • d1: Views IPM products as healthier than conventional food products

  • d2: Environmentally conscious

  • d3: Price conscious

  • d4: Familiar with IPM products

  • d5: Unfamiliar with IPM products

  • d6: Purchased IPM products in the past 6 months

  • d7: Did not purchase IPM products in the past 6 months

  • d8: Purchased organic products in the past 6 months

  • d9: Did not purchase organic products in the past 6 months.

Tables 4–6 describe the condition attributes related to the selected decision rules with the highest support [2], which are divided into blocks according to the questionnaire structure. The tables present the frequencies of the questions in each block that appear to be homogeneously distributed [3].

Based on the DRSA results, three consumer segments were identified (Table 7).

  1. Green consumers who correctly define IPM products as healthier than conventional products or consider the environment in the consumption choices or purchased organic products in the past six months

  2. IPM informed and active consumers who not only know but have purchased IPM foods in the past six months

  3. Potential IPM/low-pesticide foods or organic consumers who have no information or who have not purchased IMP or organic products in the last six months and take price into great consideration in food choices

Figure 1 presents data on the selected decision attributes and questions in the questionnaire for each of the decision rules considered. These relationships are particularly important because they confirm the complexity and heterogeneity of consumer choices with reference to different decision attributes.

On the x-axis, the extracted decision rules are decreasingly ordered with reference to the support, and thus, each decision attribute di (i=1,,9) is related to the corresponding support value. In the y-axis, the frequencies of the Bi-questions in the decision rules are displayed. For example, with a support value of 418 (the maximum), the decision rule related to d7 is based on Q8 (a question in B1, basic characteristics) and Q23 (a question in B3, perception of and attitudes toward IPM products). Note that for all the extracted decision rules with decision attribute d5, unfamiliar with IPM products, this consumer attitude is described by questions in each block Bi; that is, it relies on basic characteristics, eating and grocery shopping habits, and perception of and attitudes toward IPM products. Moreover, the number of questions involved confirmed the multifaceted nature of this decision: four in each case. The corresponding supports were in the higher 70th percentile, confirming the descriptive importance of the rules.

Again, in the case of decision attributes d1, views IPM products as healthier than conventional products, d3, price conscious, d6, purchased IPM products in the past six months, and d9, did not purchase organic products in the past six months, no question from B1, basic information, is involved in the considered decision rules.

Furthermore, if we consider the decision referring to the behaviour of having purchased IPM products in the past six months (i.e. d6), the simplicity of the extracted decision rules emerges; in fact, they are all based on a single condition attribute referring to eating and grocery shopping habits or perception of and attitudes toward IPM products.

5. Discussion

This section discusses the main results obtained by DRSA according to different decision attributes.

5.1 Segment 1 green consumer

The decision attributes d1 (Views IPM products as healthier than conventional), d2 (Environmentally conscious) and d8 (Purchased organic products in the past six months) describe the green consumer.

According to the decision attribute d1, the consumer is most likely to consider IPM products to be healthier than conventional food products if he or she is responsible for purchasing food, is indifferent to the price and prefers to buy cereals obtained with IPM methods. The second rule states that IPM products are considered healthier than conventional ones if the environment is the most important factor in purchasing decisions and if the preference is for IPM grapes. Third, the situation described by the decision attribute occurs if the purchasing decisions are not based on price, the consumer is familiar with IPM products and if he or she prefers IPM grapes.

The decision rules selected and related to the decision attribute “Environmentally conscious” share one conditional attribute: the price is not among the most important elements. The strongest decision rule describes a consumer who does not consider the price important, does not consider the appearance of the product and packaging, considers organic products to be high-quality and is willing to pay a higher price for organic grapes. The subsequent decision rules add more information. If the price and appearance are not taken into account, the product does not contain GMOs, and the consumer has correct information about IPM methods, then the consumer is more likely to be environmentally conscious. Furthermore, if the price is not decisive and the consumer resides in a Southern European country and has a monthly income equal to or greater than 5 thousand euros, then he or she is more likely to be environmentally conscious.

Moreover, consumers who have purchased organic products within the past six months are decision-makers who think that environmentally friendly production methods play an important role in choosing food items; moreover, they live alone. Similarly, a worker who purchased IPM products in the past six months was likely to have purchased organic products in the same period.

Policy implications: This is an informed market segment with low price elasticity and relatively high income, where extrinsic characteristics are of marginal importance. The products that this market segment considers important if obtained with low environmental impact processes are grapes and wine. Moreover, consumers of organic products are prevalent. Consequently, producers should equip themselves with tools (labels) capable of allowing these consumers to recognise production processes with a lower environmental impact (European Commission, 2007, 2020). From the perspective of public decision makers, dissemination campaigns using digital tools (Demartini et al., 2018) on the health and environmental benefits of consuming products with lower pesticide content could further strengthen this segment.

5.2 Segment 2 IPM informed and active consumer

The IPM informed and active consumer is outlined by the decision attributes d4 (Familiar with IPM products) and d6 (Purchased IPM products in the past six months).

Consumers are likely to be familiar with IPM products if they are aware that IPM methods require more pesticides than organic ones, if they have purchased them in the past six months and curiously are not available for training and/or additional information. Furthermore, he or she is familiar with IPM products if residing in a Southern European country, has purchased them in the past six months and is willing to pay double the price of organic leafy vegetables.

The rules' structure is very simple; only one condition attribute appears (unique case in all considered decisions): the decision maker who is not influenced by food labelling, has no restrictions in terms of the type of food production method, or thinks that IPM foods are healthier is likely to have purchased IPM food in the past six months.

Policy implications: This consumer segment has strong similarities to Segment 1 (green consumer). The rules describe an informed consumer who knows how to distinguish between different production processes and is indifferent to price and extrinsic characteristics. The strategies that can be adopted in this case are similar to those of segment 1 (labelling and digitalisation) and are attributable to tools for consolidating loyalty by both policymakers and producers, focusing on the intrinsic characteristics of IPM products.

5.3 Segment 3 potential IPM/low-pesticide or organic food consumer

The potential consumers of IPM or organic food are described by the decision attributes: d5 (Unfamiliar with IPM products), d7 (Did not purchase IPM products in the past six months), d9 (Did not purchase organic products in the past six months) and d3 (Price conscious).

It is the most important segment of our sample and describes a consumer who is misinformed, who has not purchased IPM or organic foods in the last six months and who is influenced, in purchasing decisions, by prices.

In the case of unfamiliarity with IPM, the decision rule with the highest support shows an influence of income (up to 2000 euros per month). The place of origin is not important, and he or she has not bought IPM products in the past six months because he or she is unaware of them and does not know that they are safer in terms of health. The other rules add further information on condition attributes, such as geographic area (Northern Europe), gender and other food aspects in purchasing.

The situation of not purchasing IPM products in the past six months is characterised by very high support rules (between 354 and 418); the decision-maker is not totally dedicated to work or studying, or he or she pays attention to the ways in which food is presented, or he or she believes that high-quality food undergoes a process that involves the least quantity of pesticides.

According to the decision rules, the consumer did not buy organic products in the past six months because of his or her lack of knowledge. In addition, this consumer is available to pay more for organic wine, ignores that IPM is safe to eat or is convinced that high quality means food purchased directly from the producer/farmer.

An analysis of the rules related to price consciousness reveals a consumer for whom price plays a key role: he or she did not buy organic food because it was too expensive.

Moreover, in the first decision rule, he or she declared a willingness to buy imported IPM products if cheaper, and if there is a discount. The second decision rule shows that consumers are more inclined to worry about price if they are omnivorous and if they prefer food with fewer pesticides. The last decision rule confirms the previous one, moreover he or she is not interested in IPM training information initiatives. The consumer described by the rules has high price elasticity and very moderate attention to environmental issues.

Policy implications: This is the most important segment and is numerically predominant. The decision-making rules selected highlight the lack of information of these consumers, to whom a specific campaign should be addressed to disseminate information on the environmental and health benefits derived from the consumption of food with little or no use of pesticides by policy makers. Producers of food with low environmental impacts should contribute to strengthening the knowledge of various production processes. In this context, multinational and national retail companies can assume a crucial role, which, when in direct contact with consumers, can influence their choices.

Given the importance of the public food service sector, green public procurement schemes (De Almeida Ferreira Neto and De Oliveira Gama Caldas, 2018; European Commission, 2016, 2020) in public tenders and catering services represent an indispensable tool to increase the knowledge and consumption of food with lower pesticide content.

This segment has a high elasticity with respect to price, which is decisive in consumption decisions. To influence the choices of this segment of consumers, producers should propose loyalty campaigns based on low prices, combined with tools to raise awareness of environmental protection. For this consumer segment, the extrinsic characteristics are of marginal importance.

6. Conclusions

Our results highlight the need for policymakers and food producers to take simultaneous and coordinated measures to raise consumer awareness and increase the consumption of food produced with fewer pesticides according to the market segmentation and policy implications described in Section 5. With regard to policymakers' efforts, citizens should receive more information on sustainable agricultural methods (IPM and organic), health and environmental consequences (Ajzen, 1991; Vermeir and Verbeke, 2006; Vlaeminck et al., 2014; Stranieri et al., 2017; Kautish and Sharma, 2018; Ricci et al., 2018; Petrescu et al., 2019; Bazzani et al., 2020). Therefore, control should be strengthened (Ling, 2018). Producers and the food network (Mastronardi et al., 2019), for their part, could promote information and price-based promotional campaigns to capture the share of consumers sensitive to price changes, focussing on inherent qualitative environment differences and health effects (Padel and Foser, 2005; Irianto, 2015; Kautish and Sharma, 2019). The large-scale distribution must be involved; it is the one closest to the consumer and, therefore, capable of influencing his or her choices. In view of the favourable attitude towards low pesticides products, a comprehensive strategy could be developed to promote their consumption on social and digital media for effective marketing. Moreover, our findings suggest marketers to segment the IPM food market based on consumer values and knowledge and articulate marketing strategies to convince the potential consumers about healthy and environmental benefits. According to Kautish and Sharma, “marketing activities in the green management context should focus on facilitating sustainable development experiences (e.g. consumer facilitation for used consumables, recycling behaviour and green products awareness)” (Kautish and Sharma, 2018, p. 17).

Digitisation is one of the challenges of the Green Deal (European Commission, 2019) and represents an opportunity for citizens and businesses. In the food sector, digitalisation has gained relevance only during the last few years (Demartini et al., 2018), but it is not yet sufficiently widespread, even if it represents an indispensable tool for the dissemination of sustainable practices that involve all players in the food supply chain (European Commission, 2020).

Despite the high number of our survey respondents, further research should seek to obtain data from a more balanced sample with respect to nationality and/or different regions (such as rural and urban). Moreover, different methods of analysis could be applied, and the results compared. Indeed, future research can investigate other potential decision and conditional attributes, such as intrinsic and extrinsic factors and health reasons affecting consumer choices.

In line with other research on the consumption of organic and/or green foods (Kushwah et al., 2019; Sharma et al., 2022), further studies could be promoted to systematically examine the literature on the lower consumption of pesticides, not sufficiently developed in literature and only partially addressed here.

Figures

Relationship between decision attributes and questions (Conditional attributes)

Figure 1

Relationship between decision attributes and questions (Conditional attributes)

Basic characteristics (%) of the respondents by geographic area

TotalNorthern EuropeSouthern EuropeAsiaOthera
Age18–3050.446.654.842.167.4
31–5034.339.328.242.117.4
>5015.314.117.015.815.2
GenderFemale71.476.066.050.078.3
Male28.624.034.050.021.7
EducationHigherb85.584.485.997.487.0
Lower14.516.614.12.613.0
ProfessionStudent35.527.145.536.854.3
Worker60.470.148.563.239.1
Other4.12.86.00.06.6
Income≤1,50024.530.016.323.715.2
1,501–3,00041.146.436.631.619.6
>3,00034.422.647.144.765.2
Total100.054.537.94.23.5

Note(s): a Respondents from countries belonging to North, Central, South Americas and Australia

b University education, Master's or a PhD degree

Consumer purchases of IPM or organic products within the past 6 months

TotalNorthern EuropeSouthern EuropeAsiaOther
OrganicYes71064.4%33756.1%31976.3%2360.5%3167.4%
No39335.6%26443.9%9923.7%1539.5%1532.6%
IPMYes12811.6%569.3%6214.8%513.2%510.9%
No97588.4%54590.7%35685.2%3386.8%4189.1%
Total1,103100.0%60154.5%41837.9%383.5%464.2%

Reasons (%) why consumers have purchased or would purchase IPM products

ReasonsPurchasedNorthern EuropeSouthern EuropeAsiaOtherWould purchaseNorthern EuropeSouthern EuropeAsiaOther
Ethical concerns46.154.235.65.15.144.843.948.15.03.0
Healthier products68.052.939.15.72.352.937.455.24.72.7
Better quality39.148.046.02.04.023.631.758.74.35.2
Discounted products12.550.050.00.00.08.723.571.84.70.0
Not interested in purchasing4.716.783.30.00.016.135.056.74.53.8
Other3.125.075.00.00.04.844.755.30.00.0

Selected condition attributes for the basic characteristics of the respondents

AttributeDescriptionFrequency
Q1 Gender 











67%
Q1 = 1Male
Q3 Nationality 
Q3 = 5South Europe
Q3 = 6North Europe
Q5 Family composition 
Q5 = 11 people
Q5 = 44 or 5 people
Q6 Monthly income 
Q6 ≤ 3≤ 2,000 euros
Q6 ≥ 6>5,000 euros
Q7 Profession 
Q7 ≤ 3Employee or freelance professional or entrepreneur
Q8 Time spent studying/working per day 
Q8 ≤ 3Not >8 h

Selected condition attributes for eating and grocery shopping habits of the respondents

AttributeDescriptionFrequency
Q10 Diet 















71%
Q10 = 1Omnivorous
Q12 Responsibility regarding food choices 
Q12 = 1Responsible for family food choices
Q13 Where food is purchased 
Q13_5 = 0Usually does not consider ethical purchasing
Q14 Food aspects in purchasing 
Q14_1 = 0Price is not taken into consideration
Q14_2 = 0Appearance is not taken into consideration
Q14_2 = 1Appearance is taken into consideration
Q14_3 = 0Place of origin is not taken into consideration
Q14_4 = 0Packaging is not taken into consideration
Q14_5 = 0Brand is not taken into consideration
Q14_6 = 0Labelling is not taken into consideration
Q14_ 10 = 1Environment is taken into consideration
Q15 High quality food is 
Q15_3 = 1Organic product
Q15_5 = 1Product directly sold by the producer/farmer
Q15_6 = 1Product grown with as little chemicals
Q15_7 = 1Product with no GMOs

Selected condition attributes for perception of IPM among the respondents

AttributeDescriptionFrequency
Q17 Familiarity 


























58%
Q17 = 1Familiar with IPM products
Q19 Understanding of IPM products 
Q19 = 2Greater use of pesticides than in organic agriculture
Q21 Reasons for not purchasing organic products 
Q21 = 1No knowledge
Q21 = 4Too expensive
Q23 Purchased or did not purchase IPM products 
Q23 = 1Did not purchase: no knowledge
Q23 = 9Purchased
Q24 Reasons to purchase or wish to purchase IPM products 
Q24_2 = 1Healthier than conventional products
Q24_4 = 0Not because discounted
Q25 Reasons when purchasing IPM products 
Q25 = 10Not interested
Q27 Purchased or wish to purchase imported IPM products 
Q27 = 1Imported IPM products
Q27 = 2Imported IPM products if they cost less
Q28 Perception of IPM product safety 
Q28 = 4No knowledge of IPM safety
Q32 Preferences 
Q32_1 = 5IPM cereals
Q32_3 = 6Organic tomatoes
Q32_4 = 4Organic vegetables
Q32_5 = 5IPM grapes
Q32_7 = 4Organic wine
Q34 Willingness to pay higher prices for organic products 
Q34_5 = 2Organic grapes (≤20% higher)
Q34_7 = 1Organic wine
Q34_4 = 5Organic leafy vegetables (>80% higher)
Q35 Interest in IPM method training/information initiatives 
Q35 = 2Not interested

Consumer segmentation(*)

SupportIF conditionsTHEN
1. Green consumer (d1, d2, d8)
d1: Views IPM products as healthier than conventional products
21Q14_1 = 0Q32_1 = 5Q12 = 1d1
21Q14_10 = 1Q32_5 = 5 d1
20Q14_1 = 0Q32_5 = 5Q17 = 1d1
SupportIF conditionsTHEN
d2: Environmentally conscious
30Q14_1 = 0Q14_2 = 0Q14_4 = 0Q15_3 = 1Q34_5 = 2d2
12Q14_1 = 0Q14_2 = 0Q15_7 = 1Q19 = 2 d2
12Q14_1 = 0Q6 ≥ 6Q3 = 5Q32_4 = 4 d2
SupportIF conditionsTHEN
d8: Purchased organic products in the past 6 months
65Q7 ≤ 3Q23 = 9d8
61Q14_ 10 = 1Q32_7 = 4d8
33Q14_ 10 = 1 Q5 = 1d8
SupportIF conditionsTHEN
2. IPM informed and active consumer (d4, d6)
d4: Familiar with IPM products
13Q19 = 2Q23 = 9Q35 = 2d4
13Q3 = 5Q23 = 9Q34_4 = 5d4
SupportIF conditionsTHEN
d6: Purchased IPM products in the past 6 months
101Q14_6 = 0d6
98Q10 = 1d6
79Q24_2 = 1d6
SupportIF conditionsTHEN
3. Potential IPM/low-pesticide foods or organic consumer (d5, d7, d9, d3)
d5: Unfamiliar with IPM products
102Q23 = 1Q6 ≤ 3Q14_3 = 0Q28 = 4d5
96Q23 = 1Q3 = 6Q13_5 = 0Q27 = 1d5
66Q23 = 1Q3 = 6Q1 = 1Q14_5 = 0d5
SupportIF conditionsTHEN
d7: Did not purchase IPM products in the past 6 months
418Q23 = 1Q8 ≤ 3d7
394Q23 = 1Q14_2 = 1d7
354Q23 = 1Q15_6 = 1d7
SupportIF conditionsTHEN
d9: Did not purchase organic products in the past 6 months
49Q21 = 1Q34_7 = 1d9
48Q21 = 1Q28 = 4d9
43Q21 = 1Q15_5 = 1d9
SupportIF conditionsTHEN
d3: Price conscious
45Q21 = 4Q27 = 2Q24_4 = 0d3
43Q21 = 4Q10 = 1Q15_6 = 1d3
31Q21 = 4Q27 = 2Q35 = 2d3

Note(s): (*) Selected decision rules and question frequencies if support was ≥10

Notes

1.

Our aim is not to make a distinction between behaviour and attitudes, but to describe consumer patterns with regards to IPM food products from a general point of view.

2.

The support of a decision rule is the number of elements in the Universe U satisfying both condition attributes and decision attribute considered in the decision rule.

3.

The following condition attributes are missing: education level (Q9), in the first block; the percentage of horticultural products in the diet (Q11), and the influence of family income on the quality of purchased fruit and vegetable products (Q16), in the second block; the meanings of IPM (Q18), having purchased organic products in the past six months (Q20), having purchased IPM products in the past six months (Q22), places to shop (or would shop) IPM products (Q26), reasons why IPM products are not safe or somewhat safe to eat (Q29), perception of organic safety (Q30), reasons why organic products are not safe or somewhat safe to eat (Q31), willingness to pay a higher price for organic products (Q33), in the third block.

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Acknowledgements

This article is based on research done in the context of the CITIES2030 project that has received funding from the European Union's Horizon 2020 Research and Innovation Framework Programme under grant agreement No.101000640 and on research done in the context of VERA Center, Department of Economics, Ca' Foscari University of Venice. The authors of the article are solely responsible for the information, denominations and opinions contained in it, which do not necessarily express the point of view of all the project partners and do not commit them.

Corresponding author

Maria Bruna Zolin is the corresponding author and can be contacted at: zolin@unive.it

About the authors

Paola Ferretti, PhD, is Professor of Mathematics at Ca’ Foscari University of Venice. Her main research interests cover Mathematical Methods in Economics and Finance, Decision Theory, mainly on analysis of risk and uncertainty attitude, Stochastic Orderings, in particular with reference to Multi-Attribute and Multi-Objective problems, Multi-Criteria methods.

Aiste Petkeviciute, graduated in Economics and Finance (Economics-QEM) at Ca’ Foscari University of Venice, is PhD candidate in Behavioural Finance at Louvain Institute of Data Analysis and Modelling in Economics and Statistics (LIDAM), Université Catholique de Louvain.

Her main research interests are focused on analysing retail investor behaviour regarding their portfolio diversification.

Maria Bruna Zolin is Professor of Commodity Markets and Economics of Rural Development at Ca’ Foscari University of Venice. The research activity has principally been concerned with the following items: International trade, Economics of Rural Development, Environment and Sustainable Development, Public Policies. She has served as an expert for the Food Agricultural Organization (FAO, Rome).

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