Introduction

Entomophagy that is, consuming insects as food (Chakravorty et al. 2011; Moruzzo et al. 2021a, b), has become an increasingly popular topic globally due to its potentially positive effects on food security, nutrition, and the environment (Woolf et al. 2019). As edible insects are rich in protein, unsaturated fats, vitamins, minerals, and dietary fiber, consuming them as food can greatly benefit the health of consumers if they are appropriately handled and eaten and thereby contribute to improved food and nutritional security (Belluco et al. 2013; Kinyuru et al. 2015; Tang et al. 2019). The nutritional composition of edible insects varies greatly depending on the species, life stage, habitat, and diet of the insects (Ghosh et al. 2017; Shah et al. 2022; Skotnicka et al. 2021; Tuhumury 2021). Compared to conventional livestock, edible insects have more benefits from an environmental perspective, as they require less water and soil and emit lower greenhouse gases and ammonia while producing a higher percentage of edible mass (Dagevos 2021; Lange and Nakamura 2021; Van Huis and Oonincx 2017).

Due to the abovementioned benefits, there is a growing demand for edible insects in developed countries that are not traditionally entomophagous (GMI 2020; IPIFF 2020). The value of the insect market has risen substantially from 33 million USD in 2015 to 55 million USD in 2019 in ten countries, including the United States, Brazil, and Mexico from the Americas; the United Kingdom, Netherlands, France, and Belgium in Europe; and China, Thailand, and Vietnam in Asia, and the market size is projected to further increase dramatically to 710 billion USD in 2026 (Ahuja and Mamtani 2020). As edible insects are popular on the world market and have created new income activities, numerous studies have examined their consumption and its determinants in recent years (Liu et al. 2020; Hwang and Kim 2021; Mancini et al. 2019; Moruzzo et al. 2021a, b; Omemo et al. 2021; Orsi et al. 2019; Vartiainen et al. 2020; Woolf et al. 2019). However, the majority of this research was undertaken in developed nations where edible insects are considered a novel food, and only a few studies were conducted in traditional entomophagous countries where malnutrition is often chronic, such as for example Nigeria and Madagascar (Ancha et al. 2021; Dürr and Ratompoarison 2021; Meysing et al. 2021).

Malnutrition is still a significant problem for people in the developing world (Müller and Krawinkel 2016). In 2019, approximately 687.8 million people worldwide suffered from malnourishment (Szmigiera 2021). About half of all avoidable deaths in children under five are caused by malnutrition (Bread for the world 2021). In developing countries, most people are poor and can only afford low-quality diets that contribute to all forms of malnutrition (Lartey et al. 2018; Siddiqui et al. 2020). Consequently, these countries suffer the greatest productivity losses due to malnutrition, causing a significant negative impact on their economies. Yet, nutrition is one of the most cost-effective ways to solve malnutrition and its consequences problems (Shekar et al. 2016). To address the nutritional requirements of poor people in developing countries, affordable, high-quality foods are needed (Bhargava 2015). Insects are a low-cost, high-quality, and nutritious food (Tang et al. 2019). Thus, edible insects may be a viable solution for traditional entomophagous countries where malnutrition persists. Meysing et al. (2021) pointed out that in countries with conventional insect-eating habits where chronic malnutrition is prevalent, the people’s insect consumption behavior needs to be urgently examined.

Myanmar is one of such entomophagous countries where about 30% of children under five encounter chronic malnutrition problems (USAID 2018). About 45% of deaths below five years of age are caused by various types of malnutrition (UNICEF 2014). According to Robertson et al. (2018), Myanmar suffers from both micronutrient and macronutrient deficiencies, such as protein–energy malnutrition (PEM). According to the United Nations Children’s Fund (UNICEF) definition, PEM is a hidden danger, similar to the tip of an iceberg, with dreadful ramifications that can go unnoticed (Grover and Ee 2009). Thus, PEM must be addressed to mitigate its consequences. According to the Myanmar Non-Governmental-Organization (NGO) Spectrum (2021a), edible insects are nutritious food and have the potential to substantially contribute to reducing malnutrition in the country. However, even though insects have a place in the diet of some ethnicities — mainly from mountainous areas, such as Kayin, Chin, Kachin, Shan, and others (Linn et al. 2016) — entomophagy is uncommon among urban dwellers of Myanmar’s central area (Nischalke et al. 2020). Moreover, the indigenous insect-eating culture has vanished from a number of traditionally entomophagous countries (Barennes et al. 2015; Mitsuhashi 1997; Pambo et al. 2018). Despite entomophagy has a long history in the country, still, there are no laws or restrictions on eating or collecting insects. The general lack of information on insect consumption in Myanmar poses uncertainty as to whether traditional insect consumption is disappearing in the country, emphasizing the need for consumer studies of edible insects in Myanmar. Thus, we intended to explore people’s behavior towards entomophagy and the factors that may attract or dissuade them from consuming insects as a substitute for conventional livestock and to understand the prospects of entomophagy in Myanmar by addressing the following research questions:

  1. 1.

    What is the current status of entomophagy in Myanmar?

  2. 2.

    What is the consumption intention towards edible insects, and its influencing factors?

Theoretical framework and derivation of hypotheses—literature insights

Theory of planned behavior as a theoretical framework

Various scholars have developed different models and theories to understand food consumption behavior. Among the vast array of theories and models found in the field of consumer behavior studies regarding food consumption, the Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), and Norm Activation Model (NAM) are common. However, NAM is developed to predict pro-social or pro-environmental behavior (Zhu et al. 2022), and current research is mainly interested in consumption intentions, not pro-environmental behavior. Thus, TRA and TPB are better options than NAM for this study. However, the basic idea of TRA is to explain behavior under the complete volitional control of the consumer and when dealing with persons who cannot exercise volitional control, the theory of reasoned action encounters some obstacles (Ajzen 1991; Fishbein and Ajzen 1990; Madden 1992). As this research is not restricted to examining behaviors only under complete volitional control, the TPB seemed to be the most appropriate model. Additionally, the results of the TPB research can be easily used to make interventions (Dunn 2008; Pambo 2018). Because of these considerations, TPB was selected as the study's primary theoretical foundation.

The TPB, developed by Ajzen in 1985 (Ajzen 1985), is one of the most frequently applied and tested models in predicting human behavior (McEachan et al. 2011) and is prominent in the behavioral intention studies on edible insect consumption (Mancini et al. 2019; Menozzi et al. 2017). It was originally an extended model of the TRA but modified by adding perceived behavioral control derived from self-efficacy theory to predict behavior more accurately under incomplete volitional control (Ajzen 1991; Madden 1992). It is “a full-fledged social psychology theory” that can predict human behavior (Zhang 2018, p. 1). According to Ajzen (1991), the TPB accurately predicts behavior intention with the help of attitude (ATT), subjective norm (SN), and perceived behavioral control (PBC). ATT, the first component of the TPB, indicates a person’s optimistic or pessimistic view of something or someone. The second component, SN, refers to the social influence of the surrounding people on an individual in doing something. The third component, PBC, relates to an individual’s ability to do something. In this theory, ATT is guided by behavior beliefs that refer to a person’s belief about the effects of a particular behavior (Arafat and Mohamed Ibrahim 2018). One of the main advantages of TPB is that it is flexible enough to incorporate additional constructs into the model; some researchers developed their research models by combining relevant factors adopted from different contexts pertinent to their situation to enhance and improve the predictive ability of the specific models (Bae and Choi 2020; Hwang and Kim 2021; Vartiainen et al. 2020). One of the limitations is that it is impossible to investigate the direct influence of other factors such as socio-demographic, on behavior in the absence of TPB constructs because the unique idea behind the TPB was that other elements, such as socio-demographic characteristics, should influence behavior through the components of TRA and TPB, such as attitudes, subjective norms, perceived behavioral control and intention. In other words, those factors should have indirect rather than direct effects on behavior (Shepherd and Raats 1996).

Application of the TPB to consumption intention of edible insects

The growing importance of entomophagy has drawn the attention of many researchers, who have studied its potential as food from several different perspectives (Chomchai and Chomchai 2018; Egan 2013; Pambo et al. 2016a, b; Sogari et al. 2018; Videbæk and Grunert 2020). Edible insect studies applied the TPB when predicting either consumer intention or actual behavior in entomophagous and non-entomophagous countries (Brekelmans 2016; Hwang and Kim 2021; Lucchese-Cheung et al. 2020; Pambo et al. 2016a, b). Thus, a literature overview containing a summary of research papers on entomophagy using the TPB was performed to refine our hypothesis. Table 1 lists the previous studies of edible insect consumer behavior that used TPB worldwide. It includes the countries, sample sizes, measured constructs, the focus of the study, and the significant predictors of each study.

Table 1 Summary of the worldwide studies of edible insects using the TPB
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The majority of the studies were conducted in Western societies, non-entomophagous countries (Brekelmans 2016; Lucchese-Cheung et al. 2020; Mancini et al. 2019; Menozzi et al. 2017; Navarré 2017; Vartiainen et al. 2020); with a small number of studies conducted in traditionally entomophagous countries (Bae and Choi 2020; Chang et al. 2019; Hwang and Kim 2021; Pambo et al. 2018). However, to the best of our knowledge, no consumer study focuses on Myanmar. While some of the studies only included the original constructs of the TPB—namely ATT, SN, and PBC (Brekelmans 2016; Menozzi et al. 2017; Navarré 2017)—other studies added new constructs, such as ascribed responsibility (Choe et al. 2020), phobia (Bae and Choi 2020), the interaction of self-identity and familiarity (Pambo et al. 2018), environmental concern (Chang et al. 2019), and safety (Vartiainen et al. 2020).

All these studies proved the applicability of TPB in the context of edible insect research. Moreover, the openness of adding factors to TPB components inspired us to use an extended version of the TPB. Thus, applying the TPB to the case of entomophagy in Myanmar led to the hypotheses outlined below.

Consumption intention

Consumption intention (CI) is an individual’s willingness to carry out the specific behavior that would typically occur before the actual behavior and, thus, is the best indicator of the particular action that happens (Ajzen 1991). Based on these definitions, consumption intention in this study refers to an individual’s willingness to consume edible insects in the future.

Attitude

An attitude (ATT) can be defined as a relatively static opinion of a person towards something or somebody (Solomon 2009). According to Ajzen (1991), it is one of the main indicators of behavioral intention. Attitude in this study refers to an individual’s general positive or negative opinion towards edible insects. The effect of ATT on intention was tested in various edible insect studies (Chang et al. 2019; Menozzi et al. 2017; Navarré 2017; Pambo et al. 2018; Vartiainen et al. 2020). These studies proved that ATT towards insect-based food significantly influences CI. Hence, we postulated the following hypothesis:

H1:Positive ATT towards entomophagy positively affect the CI for edible insects.

Subjective norm

Subjective norm (SN) can be defined as the perceived social pressure or influence on an individual’s particular action from the people who have a close relationship with them (Ajzen 1991); one’s attitudes are influenced by people like friends and family (Singh and Verma 2017). In the field of edible insects, researchers examined the effect of SN on behavior intention. Navarré (2017) demonstrated that SN positively affects consumption intention towards insect-based foods, which was further corroborated by Bae and Choi (2020), who additionally revealed that SN significantly influences behavioral intention towards edible insect food. Chang (2013) proved a significant causal relationship between SN and ATT. Moreover, a study of consumers’ ATT towards functional yoghurts in Vietnam verified that SN could influence consumers’ ATTs (Nguyen et al. 2020). Hence in our study, we tested for both a direct and indirect effect through an ATT of SN on CI with the following hypotheses:

H2:Positive SN regarding entomophagy positively affect the CI for edible insects.

H3:Positive SN regarding entomophagy positively affect the CI for edible insects via ATT.

Perceived behavioral control

There are circumstances in which individuals may not have complete voluntary control over their actions; consequently, perceived behavioral control (PBC) becomes an essential factor of intention as per the TPB (Ajzen 2002). It refers to individual control over performing the behavior (Ajzen 1991) and combines perceived difficulty and controllability. The former refers to a person’s perception of how easy or difficult it is to carry out a specific behavior. In contrast, the latter refers to the degree to which individuals can control their performance (Ajzen 1985, 2002, 2008, 2011). Edible insect researchers studying the importance of PBC in predicting intention all proved that it significantly impacts consumption intention towards edible insect foods (Brekelmans 2016; Hwang and Kim 2021; Lucchese-Cheung et al. 2020; Mancini et al. 2019; Menozzi et al. 2017; Navarré 2017; Pambo et al. 2018; Vartiainen et al. 2020). This led us to the following hypothesis:

H4:A high PBC regarding entomophagy positively affects the CI for edible insects.

Although TPB is broadly applied, some researchers suggested adding more constructs to it due to its low predictive ability with the above-discussed three original constructs (Karimy et al. 2015; Wang et al. 2016). There are some criteria for including additional elements in theory. The added variables should (1) be behavior-specific, (2) be the determinants of intention and behavior, (3) be independent of the existing three factors, (4) apply to a variety of behaviors, and (5) be part of the theory, and help to increase the estimation of intention or behavior (Fishbein and Ajzen 2011).

One relevant factor determining consumers’ intention to consume edible insects in studies conducted in Asia by Choe et al. (2020) and Chang et al. (2019) included environmental concern. Unlike in developed countries, in Myanmar, edible insects are mainly harvested from nature, raising environmental concerns. According to Choe et al. (2020), a person with a greater degree of environmental concern might believe that humans are gravely abusing the environment in many ways. One of the main reasons for entomophagy’s global re-emergence is that it is generally believed to cause little to no harm to the environment (Guiné et al. 2021; Imathiu 2020). However, according to Spectrum (2021b), wild harvesting of edible insects may endanger wild populations and severely affect the environment and society in Myanmar. Thus, environmental concern is assumed to be an important factor, but it is unclear in which direction they influence the CI. Hence, the TPB was modified by additionally integrating environmental concerns.

Environmental concern

Having environmental concern (EC) means realizing the harmful impacts of human actions on the environment (Kollmuss and Agyeman 2002), and was used in food studies as an additional construct to the TPB model (Basha et al. 2015; Fleseriu et al. 2020). For instance, a study on eco-friendly packaged products in India revealed that EC significantly affects buying intention (Prakash and Pathak 2017). Yet, a survey in Romania with consumers of organic products EC significantly impacted attitude, not the intention (Fleseriu et al. 2020). Although EC has become a pressing issue worldwide, edible insect studies that used the TPB as a basic model paid little attention to this factor. Chang et al. (2019) used EC as additional TPB constructs but found no impact of them on buying intention of edible insects in Taiwan. Similar studies from developing countries have not addressed the effect of EC on the CI of edible insects. As insect consumption in Myanmar is largely dependent on wild collections, we tested the effect of EC with the following two hypotheses:

H5:High EC has a negative direct effect on the CI for edible insects.

H6:High EC has a negative indirect effect on the CI for edible insects via ATT.

Background factors (moderators)

According to Wassmann et al. (2021), most edible insect studies neglected to test the moderating effects of factors such as age and education. Although organic food studies and other studies usually tested the moderating effect of different factors in the relationship between TPB constructs and intention (Asif et al. 2018; Saleki et al. 2021; Tandon et al. 2020; Tarhini 2013; Wang et al. 2019), only a few edible insect studies have considered moderating variables (Hwang and Kim 2021; Navarré 2017). Hwang and Kim (2021) tested the moderating effect of product knowledge in the relationship of all TPB constructs on behavioral intention to use edible insects in restaurants. Product knowledge moderated the relation of subjective norms and behavioral intentions but not the other constructs.

A moderating variable, or moderator, is a factor that moderates the influence of an independent variable on a dependent one, which is termed the moderator effect (Edwards and Lambert 2007; Hair Jr et al. 2017; Preacher et al. 2007). According to Memon et al. (2019), a compelling reason for a moderation analysis can be the contradictory effects of independent variables on a dependent one. The effects of individual factors in edible insect studies have revealed inconsistent findings. For instance, Vartiainen et al. (2020) found that individuals from a Western society with higher education were more likely to consume edible insects, contradicting findings from studies in other Western countries (Hartmann et al. 2015; Tan et al. 2017). Although in Western countries, men are more likely to consume edible insects than women (Castro and Chambers 2019; Menozzi et al. 2017), in some entomophagous countries such as China, women consume more insects (Castro and Chambers 2019), though in others like Korea or Ethiopia not (Ghosh et al. 2020). Similarly, other sociodemographic factors, such as age, caused differing results on insect consumption even within the same country (Hlongwane et al. 2021). These contrasting findings stress the need for moderating analyses of sociodemographic factors. Thus, we tested the effect of background factors such as gender, age, education, income, location, administrative division, ethnicity, religion, family size, and experience with insect consumption on intention to consume edible insects using the following hypotheses:

H7a: Background factors moderate the relationship between ATT and CI for edible insects.

H7b: Background factors moderate the relationship between SN and CI for edible insects.

H7c: Background factors moderate the relationship between PBC and CI for edible insects.

Figure 1 shows the conceptual framework of the study.

Fig. 1
figure 1

Conceptual framework for testing hypotheses (modified after Ajzen (2002))

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Material and measures

Measures

In order to test the hypothesized model presented in Fig. 1, we used a Structural Equation Modelling (SEM) approach. Each of the five latent constructs (ATT, SN, PBC, EC, and CI) was reflectively measured with three indicators, utilizing a 5-point Likert scale (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree and 5 = strongly agree). The indicators for the constructs were adapted from scales used in previous research and slightly adjusted to the context of our study (Table 2). The items to measure the constructs ATT and PBC were adapted from Wang et al. (2019), referring to the studies of Ajzen (2002) and Asif et al. (2018). Chang et al. (2019) cited the study of Dunlap and Van Liere (1978) as the source for the items used to measure CI and EC. An adapted scale from Vartiainen et al. (2020) was used to measure SN.

Table 2 Description of questionnaire structure and supporting literature
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Survey design and data collection

The items and questions used in the survey were translated from English into Burmese and back-translated into English with the help of a translator to check whether the concept and linguistics were identical. The questionnaire included two parts: part one was related to background factors, and the other was for measuring TPB constructs.

Data were collected by telephone surveys between March 2021 and June 2021 with citizens of Myanmar. As there are no official lists of cell phone numbers in Myanmar, a random sample of cell phone numbers were generated for conducting the telephone survey. The cell phone numbers in Myanmar usually have eleven digits: the first two digits, “09,” are a prefix for all cell phones, the third digit is the cell operator code, and the following three are used to identify the region. The remaining five digits were added randomly. In total, 18,694 cell phone numbers were dialed. Of the contact numbers, 68% were invalid, unavailable, or not yet installed; 14% of the contacted were unwilling to participate in the interview, 13% did not answer the phone call, and only 5% agreed to participate. Among these 949 volunteers, 53 respondents refused to answer more than half of the questions. Thus, finally, collected data from 897 respondents were entered into an SPSS worksheet.

Data analysis methods

The characteristics of the respondents and the main measures were described through descriptive statistics and SEM using SPSS 25 and AMOS 24. Structural Equation Modelling is a multivariate tool incorporating confirmatory factor analysis (CFA) and path analysis into a single framework for testing hypotheses about the interactions between factors (Altikriti and Anderson 2020; Hasman 2015). Confirmatory factor analysis was conducted to validate that the SEM met the requirements of validity and reliability (Awang 2014). After assessing the measurement model's validity, reliability, and model fitness, the model fitness of the SEM was tested, and causal effects were analyzed using path analysis. Path analysis determines the structural relationships between observed and unobserved factors (Altikriti and Anderson 2020).

After that, the moderating effects of background factors on the SEM were examined using multi-group moderation analysis because Ajzen and Albarracin (2007) acknowledged the indirect effect of background factors on behavior via attitude, subjective norm, and perceived behavioral control. Moderating analysis determines whether or not two constructs have the same relationship across different groups (Memon et al. 2019). Thus, in this analysis, each background factor was divided into two groups; after that, the identical model was evaluated for each group to explore the significant differences between the two groups by performing pairwise comparisons across the models.

Results

Sample description and descriptive results

The number of participants in the final dataset was reduced from 897 to 872 after inaccurate or incomplete data were removed. This data covers Myanmar's all areas and represent the whole country (OSF Appendix 1 and 2). In Myanmar, there are 15 administrative divisions; the areas where the country’s largest ethnic group, the Burmese, reside are referred to as regions. On the other hand, the areas inhabited by other ethnic groups—namely the Kachin, Kayah, Kayin, Chin, Mon, Rakhine, and Shan—are referred to as states. Briefly, in regions, the Burmese are a majority, while other ethnicities are minorities; in states, it is the reverse. Hence, the collected data included all ethnic groups.

As presented in Table 3, the majority (71%) of respondents lived in regions, whereas the rest lived in states. One-third of them were rural residents, and two-thirds were urbanites. The sample comprised an almost equal ratio of males to females and of age groups (the latter in terms of under 30 and over 30 years old). About 85% of respondents earned less than 200 USD per month, while 15% earned more than this. The respondents’ education level was high, with 68% having a university education. The majority (68%) were Burmese, whilst 32% belonged to other ethnic groups. In terms of religion, about 88% were Buddhist, and the remaining 12% were composed of Christians (9%), Hindus (1%), and Muslims (2%). Concerning family size, most (74%) of the population came from a family of four or more members.

Table 3 Responses to consumption intention indicators by participants’ background factors
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Except for gender and consumption experience, there is no variance in the mean values of CI indicators among groups of each background factor. Consumption intention slightly varied by gender and strongly by consumption experience, with women having lower response rates than men. People who had eaten insects in the past had a mean CI-1 and CI-2 score of 3.5, indicating that they intend to eat insects again within the next three months. However, given that their responses had a mean CI-3 value of 3.0, they were unsure if they would advise others to eat insects. While those who had not eaten insects gave answers in the range of 2.1–2.2, indicating that, on average, there was no intention to eat insects nor to suggest them to others. For all other background factors, the means of CI-1 ranged from 3.1 to 3.3, whereas those of CI-2 and CI-3 ranged from 3.0 to 3.2 and 2.5 to 2.9, respectively. As the mean response level leaned towards neutral, overall, there was uncertainty about whether to eat insects in the next three months or to recommend this.

Distribution Percentages of Likert scale responses for all indicators

Even if more than 70% of respondents had tried edible insects, their CI was not high, with the means of items ranging from 2.7 to 3.1 (Table 4). As respondents tended to the neutral answer, they were either uncertain or undecided in their statements. The same held true for AAT, SN, and PBC. The mean values of all indicators ranged from 3.1 to 3.4, but participants’ responses to the indicators of EC leaned towards the agreement, with the means of items ranging from 4.1 to 4.3.

Table 4 Distribution Percentages of Likert scale responses for all indicators
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Confirmatory factor analysis

As a first step, a CFA was conducted to validate that the model met the requirements. As shown in Table 5, the factor loadings of each indicator were > 0.5, after eliminating SN-3. Cronbach’s alpha and composite reliability (CR) results were between 0.74 to 0.89; thus, all constructs in this study had good internal consistency. Moreover, the average variances extracted (AVE) result ranged from 0.51 to 0.74, meaning the model achieved convergent validity. And also √ AVE was greater than the correlation for each pairwise construct (off-diagonal), so discriminant validity was given.

Table 5 Analysis results of confirmatory factor analysis
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The fit indices for the model, including all indicators, showed that x2/df was 3.58, thus above the acceptable value of 3. The model with the exclusion of SN-3 resulted in an acceptable range for good model fit based on Kline (2016), as shown in Table 6. In sum, the CFA findings indicate that the measurement models were valid for processing the path analysis (structural model).

Table 6 Fit indices of the measurement model
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Structural equation model

The results of the overall goodness-of-fit of the hypothesized structural model showed acceptable to good results, with \({\chi }^{2}\) = 194.13, df = 68, p < 0.001, \({\chi }^{2}\)/df = 2.86, RMSEA = 0.05, AGFI = 0.95, CFI = 0.98, NFI = 0.97 and TLI = 0.97.

Five out of six hypotheses were not rejected, as shown in Fig. 2. The unstandardized coefficient is shown with the standardized coefficient in parenthesis. Significant standardized coefficients below 0.1 are classified as small (S), between 0.1 and 0.2 as medium (M), and above 0.2 as large (L) (Mehmetoglu and Jakobsen 2017). ATT towards eating edible insects had a significant positive influence on the CI of edible insects (b = 0.75, p < 0.001) and was the highest predictor for the CI. It also mediated the relationship between SN and CI. There were significant moderate positive effects of SN (b = 0.15, p < 0.01) and PBC (b = 0.13, p < 0.01). EC had a negative effect on CI (b = -0.23, p < 0.01), but the effect was small. Briefly, CI was significantly influenced by all four constructs, with a R2 = 0.469; TPB constructs—namely ATT, SN, PBC, and the additionally included construct EC —explained 47% of the variation in CI to eat edible insects, which according to Chin and Newsted (1998) reflects a model with moderate explanatory power.

Fig. 2
figure 2

Results of structural model analysis

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Multi-group moderation analysis

The moderating effects of ten background factors were examined using multi-group moderation analysis to analyze the variations between the same model for diverse groups. Each background factor was divided into two groups. Each factor was added to the model one at a time and tested separately in order to test the increasing or decreasing effects of the dependent variable on an independent variable. According to Hair Jr et al. (2020), a model has to be well-fitted for a correct interpretation of the results. All models of the tested categories except for income and religion showed an acceptable model fit (OSF Appendix 3). Some indices of higher income and other religious groups were not satisfactory because the sample size for these groups was lower than recommended (200). The normal fit index (NFI) of the higher income group was 0.894; still, the interpretation was meaningful, as the other seven indices were satisfactory. Also, the goodness of fit (GFI), NFI, and the root means square error of approximation (RMSEA) of other religious groups were not satisfactory. However, the interpretation for this group was meaningful because at least one index of absolute fit, parsimonious fit, and incremental fit was in the acceptable range. A two-tailed z-test’s z-score was used to determine the significance between groups. The absolute value of the z-score > 1.96 is considered significant at the 0.05 level, > 2.57 at the 0.01 level, and > 3.28 at the 0.001 level (Afthanorhan et al. 2014; Goss-Sampson 2018; Weston and Gore 2006).

Table 7 shows that the administrative division moderated the effects of ATT and PBC on CI, with a z-score > 1.96. Although ATT→CI was significant for both state and region, the effect was more visible among respondents from regions while the PBC effect on CI was stronger for states. Regarding location, SN had a significantly stronger positive effect on CI for urbanites but not rural people. Education amplified the impact of ATT on CI for respondents with a university education more than those with only a high school diploma or less. Ethnicity moderated the PBC→CI relationship, with a stronger impact on the other ethnic groups, meaning ethnicity had a more intense moderating effect on non-Burmese people. Gender, age, income, religion, family size, and consumption experience had no significant moderating effect on any relationships since all z-scores < 1.96. Since, R2 values for each group of all factors were ranging from 0.36 to 0.63, each model has moderate explanatory power.

Table 7 Results of moderating analysis
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Discussion

Only less than half of the respondents in our study in Myanmar exposed their intention to eat insects. On average, participants' responses to ATT, SN, and PBC towards entomophagy leaned towards neutral answers. Only EC and consumption experience showed considerable variation in CI. Expectedly, people with prior insect consumption experience have a higher CI than those without. Pambo (2018) showed that intentions to consume insects are affected by a lack of consumption experience, and behavior is associated with past experiences and the inclination of an individual to act (Haddock and Maio 2007).

A significant positive effect of ATT on CI is in line with previous entomophagy studies (Chang et al. 2019; Pambo et al. 2018). As edible insects are often perceived as a gift of nature in rural areas, people who cannot always afford to buy other foods are more accustomed to eating insects collected in the wild. Additionally, although insects can be expensive at markets, people buy them due to their traditional habits. Some people might also realize the nutritional benefits of edible insects, thus exhibiting a positive attitude towards entomophagy. As Çoker and van der Linden (2022) mention, if ATTs towards edible insects are more optimistic, CI will be higher.

The positive effect of SN on CI indicated that either close or important persons influenced the respondents’ insect CI. If people who were important to respondents consumed insects enjoyably, they were more likely to eat insects themselves, reflecting the social influence emanating from the people around them. The positive effect of SN on CI towards edible insects is aligned with previous entomophagy studies (Hwang and Kim 2021; Piha et al. 2018; Verneau et al. 2016). SN had a direct and an indirect effect on CI through ATT, corroborating earlier results from other studies in Asia (Bae and Choi 2020) and may be explained by the fact that people around can greatly influence individuals’ behavior, meaning social pressure is essential in shaping a person’s ATT (Riemer et al. 2014).

We found a significantly positive effect of PBC on CI, similar to an earlier study in Kenya (Pambo et al. 2018). According to our descriptive statistics results, only half of the respondents might have the ability to decide to consume insects independently; the other half did not have enough time and money to search for, buy, and eat insects and might be uncertain about the perceived difficulties of doing so. The lack of respondents’ own decision-making, confidence in their ability, and perceived difficulties become significant hurdles in the CI for edible insects. Among these three items, perceived difficulty may be the main barrier. Although offline and online insect markets are well-developed in some big cities in Myanmar, such as Yangon, Mandalay, and Bago, edible insects are unavailable all the time and at all locations. The seasonal availability of edible insects may make it challenging to obtain them in the off-season (Barennes et al. 2015). Consequently, people who consider that searching (either collecting or buying) for insects is not a burden are more likely to eat them.

Confirming one of our initial hypotheses, EC leads to a lower CI for edible insects. As more than 90% of respondents showed serious EC, they might have realized the adverse effects that consuming insects can have, such as the extinction of species due to overharvesting and other harms to the ecosystem (Spectrum 2020b). According to Linn et al. (2016), ecological problems might arise due to over-harvesting and cutting down of host trees in search for edible insects, contrasting the often-cited notion that edible insects are an environmentally friendly food. Yet, because of the rather small effect that EC had in our study on the CI for edible insects, there may be some other factors which were not covered here such as food insecurity and usual habits that govern the behavior of consumers.

Only four out of the ten investigated background factors had a significant moderating effect on CI. Administrative divisional differences had a moderating impact on the relationship of ATT and PBC on CI, with stronger effects on respondents from regions than states of Myanmar. Entomophagy is not such a common practice in the country’s regions, so the CI might mainly depend on the ATT. On the other hand, the effect of PBC on CI was 16 times stronger for respondents from states than those from regions. As insects are proliferous throughout Myanmar, they can be collected relatively easily in rural areas, whereas urbanites have primarily the option to buy. As edible insect markets (traditional and online) are more developed in the regions such as Yangon and Mandalay (Spectrum 2020a, c) than in the states, it is easier to buy insects there. Moreover, as most Burmese live in the regions, whereas other ethnicities live in the states (Myanmar embassy (Tokyo) 2003), administrative divisional effects are amplified by ethnic differences. Consequently, the effect of PBC on CI was ten times stronger for non-Burmese ethnicities, meaning that for non-Burmese respondents living in the states, the perceived difficulties are more crucial for their CI for edible insects.

An urban location enhanced the effects of SN on CI. Because of the greater food options in urban areas, insects are a less important food item than in rural areas. Thus, in rural areas where entomophagy is common and food security a major issue, the CI depends less on socio-psychological factors, whereas, in urban areas, the SN play a more prominent role.

Education amplified the effect of ATT on CI. Education seems critical in fostering a wider acceptance of entomophagy, as it can change people’s ATT (Petersen et al. 2020). A better knowledge of the nutritional benefits of insects is significantly related to higher education (Cicatiello et al. 2016; Reverberi 2021); this might lead to positive ATTs and higher CI.

Gender, age, income, education, religion, family size, and consumption experience had no moderating effect on any relations. However, differences were found in each association for each group. For example, path coefficients for SN and EC for females significantly differed from zero. Thus, significant relationships were found in SN → CI and EC → CI for females but not for males. At the same time, the path coefficient for PBC for males was significantly different from zero. Thus, significant relationships were found in PBC → CI for only males. Though gender did not moderate the effects of the TPB's factors on CI, both groups could not be considered homogenous. It is the same for other background factors, thus, our results stressed the importance of moderating analysis for this study. Notably, no background factors moderated the relationship between EC and CI. This might be because most respondents, regardless of their background, showed high EC vis-à-vis the consumption of insects.

Our SEM's moderate explanatory power proved that TPB is a suitable framework for this research. Various edible insect studies have shown that the TPB explained 17–80% of the variations in CI towards edible insects (Brekelmans 2016; Hwang and Kim 2021; Mancini et al. 2019; Menozzi et al. 2017; Pambo et al. 2016a, b; Pambo 2018; Sogari et al. 2019; Vartiainen et al. 2020). As the TPB explanatory power in our models with or without moderators are well in the range of the just cited other studies, we can confirm that the TPB is an appropriate framework for identifying factors that influence the CI to consume edible insects in Myanmar and beyond. Moreover, in this study, the addition of a new variable (environmental concern) to the TPB model fulfilled the criteria stated by Fishbein and Ajzen (2011). These criteria included: (1) the new variable must determine intention, (2) it should be independent of the existing three factors, (3) it should apply to a range of behaviors, and (4) its inclusion, in theory, should enhance the estimation of intention.

Our study is not without limitations. The first limitation refers to the data collection method. Although telephone interviews are cost-effective, they did not well represent the rural–urban population, with disproportionally more respondents from towns than the countryside. Additionally, most respondents were Buddhist, with Hindus and Muslims being underrepresented in our study. Thus, future research should ascertain more representation from rural parts of the country and religious minorities. Another constraint is that our study focused on the intention to consume edible insects in the following three months and failed to explore whether a future CI turns into actual consumption and whether the factors that affected the intention also affected the real actions. Insect consumption in Myanmar mainly relies on wild insects, and most insects are seasonal—usually only available once a year for a short period of time. Thus, CI for different times of the year should be explored. Moreover, future studies should conduct a deeper analysis of consumers’ behavior towards each specific species, form, and type of edible insect. As Norberg et al. (2007) stated, the intention is not always followed by real performance; hence, an investigation of actual behavior needs to be carried out to validate our findings.

Conclusion

As the first consumer analysis of edible insects in Myanmar, this study supported the appropriateness of the theory of planned behavior for analyzing CI for edible insects in the country. In addition, we could show the influence of environmental concern on behavior intention and thus add a new dimension to the TPB. Therefore, broadening the scope of the TPB model is possible for future edible insect research. Our findings on the negative effects of EC can be valuable information for entrepreneurs considering entering the insects for the food market and those already in the business. As this negative impact is primarily determined by the fact that insect consumption in Myanmar mainly depends on the collection of wild insects, this highlights that transforming wild harvesters into insect farmers is urgently needed in Myanmar to incorporate insects into a more sustainable food system. This should go along with rising awareness that insect farming can reduce the environmental consequences of overharvesting wild insects. Market players should take advantage of consumers’ EC by, for example, stressing how their consumption affects the environment. This will increase consumer awareness of the consequences of collecting wild insects vis-à-vis reared insects.

The significant effect of ATT on the intention to eat edible insects highlights the need to better inform consumers of the many benefits of entomophagy, for instance, by means of media, public forums, or the distribution of brochures. One of our key findings is that SN is positively related to both ATT and intention, which can be used to promote insect consumption in Myanmar, for instance, on social media. As administrative division and education level moderate the relationship between ATT and CI, forming a positive ATT by providing information on the nutritional benefits of insects is especially important for individuals from regions and with higher education. As administrative division and ethnicity are moderators of PBC and CI, insect farming should be accelerated, especially for non-Burmese ethnicities who live in states, to improve access to edible insects beyond wild harvesting and throughout the year. The urbanites also need promotional efforts to strengthen a positive relationship between SN and CI.

Although insects are available throughout the country, insect availability is still limited due to seasonality and the underdevelopment of insect farming in Myanmar. Thus, the perceived difficulty of consuming insects by many might be reduced by rearing insects to create a constant supply. Accelerating insect rearing can also provide a greater supply during the off-season, thereby increasing insect consumption. Not only the availability of edible insects but also their accessibility in many markets throughout the year with affordable prices might increase the frequency of insect consumption. So, edible insects can help to reach the nutrition policy's broad goal of reducing malnutrition in all its forms by making it easier for all people, at all times of the year, to get nutritious food at an affordable price. In this way, edible insects may have both direct and indirect effects on achieving the Sustainable Development Goals (SDGs) like ending hunger (SDG-2), ending poverty (SDG-1), having decent work and economic growth (SDG-8), having good health (SDG-3), being responsible consumption and production (SDG-12), and taking action on climate change (SDG-13).