Introduction

Background and aim

In addition to reducing littering, the amount of plastic used and the amount disposed of as waste should be reduced to resolve the issues of marine litter and ocean plastic. A report revealed that over 80% of ocean plastic originated on land [1]. In a survey covering both the land and the waters of the coasts, rivers, and lakes of Japan, a numerical calculation of litter items collected revealed that over 90% of the litter originated on land, excluding scraps and fragments [2]. Concerning the litter drifting in the coastal sea areas, most of it originates domestically, comprising litter from land washed into the seas through rivers. Therefore, it is urgent to control litter generation around rivers, including inland areas [3].

When seeking effective resolutions against littering, understanding individuals’ behavioral mechanisms and processes is crucial. To prevent littering, direct and immediate measures imposing sanctions, such as strengthening monitoring and penalties, might be useful. However, such sanctions have limitations owing to the impracticality of installing security cameras across all sites and the associated huge costs. Therefore, interventional measures have been developed to change behaviors without relying solely on sanctions [4,5,6]. For example, transitioning from disordered surroundings to orderly ones is effective in reducing littering [7,8,9,10].

Although, such interventional studies have presented significant findings and efficient measures, they alone have limitations in uncovering the psychological process leading to littering behavior. Hence, the psychological process should be examined. Mere appeals to the moral conscience are unlikely to reduce environmentally unfriendly behavior if an appropriate process model is not developed [11]. Therefore, studies on pro-environmental behavior, introduced in the following section, have identified the psychological factors relevant to predict the behavior and have developed the models predicting the behavior. A model specific to littering behavior should be constructed to explain the process leading up to littering, applying the existing studies and models on pro-environmental behaviors.

Conversely, only enumeration of psychological factors alone cannot provide useful measures to promote pro-environmental behaviors and to prevent anti-environmental behaviors because they often neglect the situational factors that arise in a specific context that are directly connected to a specific behavior [12,13,14,15]. The model integrating attitudinal and situational factors would be more useful for considering efficient concrete intervention measures, bridging the studies of interventions in the field setting and those explaining psychological mechanisms. Accordingly, this study aims to develop a behavioral model specific to littering considering existing pro-environmental behavior models involving situational factors in addition to existing attitudinal factors.

Selection of a pro-environmental behavior model

Pro-environmental behavior models targeting various behaviors have been proposed, such as the promotion of reduce, reuse, and recycle (3 Rs), energy conservation, and transport behavior [16,17,18,19]. In this study, a model specific to littering behavior is constructed based on the stage model of self-regulated behavioral change (SSBC) proposed by Bamberg [12, 20]. A stream of pro-environmental behavior studies has been based on the theory of planned behavior (TPB) [21,22,23]. The TPB posits that behavioral intention is the direct determinant of behavior and that behavioral intention based on attitude, subjective norms, and perceived behavioral control determines target behavior [24]. The norm activation model [25] and value belief norm theory [26], which address internalized personal norms, have also been used as models to explain pro-environmental behavior. Hirose [27] merged these models to propose a two-stage model constituting goal intention and behavioral intention, which lead to behavior. The two-staged model posits that attitude and goal intention can be replaced. The determinant of goal intention is founded on the norm activation model and value belief norm theory, and the determinant of behavioral intention is founded on the TPB.

However, with the increase in the body of research on pro-environmental behavior, the strength of the association between behavioral intention and behavior has been inconsistent [28]. The association between behavioral intention and behavior can be weakened if, for instance, the behavior is difficult to implement, if the behavioral change takes time, or if a situation that satisfies certain requirements does not appear [13, 17]. Bamberg [12, 20] proposed the SSBC for explaining the gap existing in case of a weak association between behavioral intention and behavior, which makes it possible to determine ways to eliminate that gap. He hypothesized that implementation intention exists between behavioral intention and behavior. Implementation intention refers to an intention manifested in specific situations that satisfy requirements relating to when, where, and how the behavior is manifested [15]. The SSBS model incorporates implementation intention into the TPB and the norm activation model. The SSBC is used in relation to habit change and behaviors that require time for a decision to be made, such as the purchase of an environmentally friendly automobile or electrical appliances [29, 30]. The behavioral domain used SSBC are categorized by two types of decisions; low-cost and repeated, and high-cost and investment [31]. Examples of the former ones are the reducing reduction of car use and consumption of environmentally hazardous beef, of which habit weakens the link between behavioral intention and behavior, and thus, environment-unfriendly behaviors are repeated. Although littering behaviors, the focus of this study, are not necessarily generated by habits, the SSBC is applicable to littering behavior because the functions are similar to habitual behavior. That means that littering behavior is low-cost and repeated, and anti-environmental behavior is triggered due to the weak association between pro-environmental behavioral intention and behavior. Moreover, littering behavior occurs in specific situations even if the individuals have pro-environmental attitudes. Particularly, the SSBC is useful for explaining the gap between behavioral intention and behavior by placing the implementation intention that is a critical point for understanding littering behaviors.

The SSBC theorizes that pro-environmental behavior is attained through goal intention, behavioral intention, and implementation intention. As a person progresses through the stages of intention, the details of the target behavior become more tangible, and a more detailed plan is formed [12]. The determinants for each intention are placed with the relevant antecedent factors. The determinants for goal intention are a seriousness perception, a sense of own responsibility, and personal norm used in the norm activation model [25] and value belief norm theory [26]. The subsequent stage of behavioral intention is impacted by determinants such as perceived behavioral control used in the TPB [21]. Furthermore, in the stage concerning implementation intention, plans relating to when, where, and how to behave have effects, which ultimately lead from implementation intention to behavior.

Behavioral model constructed in this study

Construction of the model of non-environmental behavior

As with existing models, the SSBC model explains processes leading up to pro-environmental behavior. To construct a behavioral model specific to littering behavior, in addition to the discrepancy between attitude and behavior, investigating the discrepancy between behavioral intention specific to littering and behavior is crucial. In other words, it should be explained as to how a person with pro-environmental intentions, who does not intend to litter, can engage in non-environmentally friendly behavior (i.e., littering). In terms of intentions, this study defines goal intention as a general pro-environmental attitude and behavioral intention as the intention to avoid littering. Meanwhile, littering behavior, the focus of this study, has a negative orientation that is not pro-environmental. Therefore, in the overall model, even if there is a pro-environmental intention of “I do not intend to litter,” this can result in the person engaging in anti-environmental behavior in the case of littering. This gap is not addressed in conventional pro-environmental behavior models. Thus, constructing a new model is crucial to predict anti-environmental behavior.

This study posits implementation intention to explain this gap. An implementation intention is defined as an intention to tolerate littering that arises in specific contexts. We hypothesize that littering behavior is influenced by implementation intention. Therefore, treating implementation intentions related to littering as environmentally unfriendly is appropriate. Examining littering behavior should encompass diversity and specificity in behavioral contexts. Bamberg demonstrated that a relevant factor was to have specific plans about when, where, and how to behave to evoke implementation intention [12, 20]. Similarly, this study assumes implementation intention to be a direct determinant of littering behavior, exerting a greater impact than behavioral intention in a particular situation. Specifically, if the situation is conducive to littering (e.g., litter is scattered and there are no garbage cans nearby), then this may increase implementation intention.

Considering these aforementioned concepts, a negative relation is expected to be observed between pro-environmental behavioral intentions and anti-environmental implementation intentions and between pro-environmental behavioral intentions and anti-environmental behaviors. This is because the direction of the concepts is opposite. However, no relation might be observed instead of negative relations among them because positive pro-environmental intention might not relate to the triggering of implementation intention in specific contexts and anti-environmental behaviors. A positive relation is expected to be observed between anti-environmental implementation intentions and anti-environmental behaviors, as they are oriented in the same direction.

Determinants of goal intention and behavioral intention

The SSBC model predicts pro-environmental behavior; however, further verifications are required because no empirical evidence exists on whether it can predict littering behavior. In addition, the relevant factors, specific to the topic of littering, associated with goal intention, behavioral intention, and implementation intention, should be examined. This study considers factors depicted in Fig. 1.

Fig. 1
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Model of littering behavior in this study

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The antecedent factors of goal intention examined in this study include “awareness of negative consequences,” “perceived personal responsibility,” and “personal norm,” founded on the norm activation model. Following the SSBS, these three factors are sequentially associated [20]. The antecedent factors of behavioral intention include, as in the two-staged model, goal intention (i.e., one’s general attitude), perceived behavioral control (i.e., one’s evaluation of the simplicity/complexity of the behavior), and social norms. While there exist multiple subconcepts regarding social norms, this study focuses on the injunctive norm. Social norms encompass descriptive and injunctive norms. Descriptive norms refer to “what most other people do,” while injunctive norms refer to the belief that one should conform to rules seen as socially correct [5]. By contrast, the TPB posits that subjective norms, defined as the expectation received from significant individuals, impact behavioral intention. Both subjective and injunctive norms are similar in that they are expectations from surrounding people, yet they differ in that the expectation is limited either to significant individuals or extended to a wider range of people [32]. Among these social norms, in constructing the littering behavior model addressed in this study, the consideration of the injunctive norm is justified for two reasons. First, the descriptive norm is unsuitable because it may have a negative impact, given that most people do not engage in pro-environmental behavior. Therefore, it is unlikely to serve as a determinant of pro-environmental behavioral intention [31]. Second, it is crucial to evaluate whether littering behavior is viewed as preferable by the general population rather than solely by certain significant individuals. Therefore, this study positions the injunctive norm as the social norm associated with behavioral intention.

Relationships of demographics and lifestyle with littering behavior

Effects of personal attributes (i.e., demographic factors) and lifestyle should be considered in addition to intentions and their antecedent factors for a satisfactory explanation of littering [33]. For instance, some studies have revealed that gender affects the tendency to litter [34]. Other studies have indicated that gender has little effect on predicting littering behavior [33]. Furthermore, studies have reported that younger people more likely litter than older people [35]. Other reports have demonstrated that smokers often litter [36]. Therefore, these demographics and lifestyles should be factored in to complement the investigation.

Measuring non-environmental behaviors and intentions using a questionnaire

Littering is a socially undesirable behavior. Most studies on littering behavior are based on observation surveys [5]. Observation surveys directly ascertain the realities of socially undesirable behavior and investigate intervention measures suited to that situation. However, observation surveys cannot be used to understand the intentions and attitudes determining behaviors. Questionnaires are the standard technique used to understand the intentions and relevant antecedent factors internal to individuals. Nonetheless, it is necessary to focus on the idea of whether socially undesirable behavior can be accurately measured through a questionnaire. When using question items such as “Do you litter?” or “Have you ever littered?” to measure behavior, the risk of an excessive bias exists. Furthermore, the answers will be geared more toward what is socially preferred than they will be toward reality [37]. It is the same when setting questions regarding implementation intention. Accordingly, the questions must be carefully designed to prompt the respondent to recall actual behavioral scenarios and give an honest response. In the study, an effort was made to elicit honest littering experiences using expressions such as “In this setting, have you ever done X?” in the form of behavioral questions. For the items on implementation intention, respondents were presented with specific situations and asked to judge whether littering would be feasible. The specific questions are stated in the Methodology section.

Purpose

This study investigates the validity of a model for explaining the processes leading up to littering behavior by applying the SSBC framework. In the proposed model, in addition to goal intention and behavioral intention shown in the existing pro-environmental behavior model, implementation intention (i.e., scenario-specific intention) is included in the investigation. These intentions influence one another in the sequence of goal intention, behavioral intention, and implementation intention, thereby leading up to the behavior. Although the model’s fitness is tested by including all variables depicted in Fig. 1, the focus of the analysis is to examine the relations among pro-environmental behavioral intention, anti-environmental implementation intention, and anti-environmental littering behavior. More concretely, the hypotheses proposed are as follows: a) anti-environmental implementation intention is directly positively associated with littering behavior; b) pro-environmental behavioral intention is negatively associated or not associated with anti-environmental implementation intention; and c) pro-environmental behavioral intention is negatively associated or not associated with littering behavior. Moreover, d) the connection between implementation intention and behavior is stronger than that between behavioral intention and behavior. In addition, an exploratory investigation is performed to identify the connection between the attributes of people who litter and their littering behavior.

Methodology

Summary of survey implementation

An online questionnaire survey was conducted to construct a model to predict littering behavior. The survey period was from October 15 to October 18, 2021. The respondents were recruited as online respondents from Cross Marketing Group Inc. The eligible respondents were aged 18 years or above. They were divided by gender and age to resemble the population distribution. A total of 5000 responses were collected in line with the population of each Japanese prefecture.

After collecting the data, screening was conducted on the basis of the five perspectives shown below, excluding responses that met any of the criteria: (1) if the same answer was given to every question in the survey; (2) if the same answer was given to every question in the survey except for one or two instances; (3) if the same answers were given to all questions within one category; (4) if the answers comprised just two digits that suddenly changed at one point and remained consistent thereafter; and (5) if the answers were given following some sort of regular numerical pattern. The final number of valid responses after the screening process was 4642.

Survey questions

The questions were produced to reflect potential littering scenarios in daily life. The variables of goal intention, behavioral intention, and their antecedents were set up with reference to those used in previous studies [38,39,40]. There were two questions on goal intention. The antecedent factors of goal intention included awareness of negative consequences, perceived personal responsibility, and personal norms, and two questions were asked concerning each of these factors. There were two questions on behavioral intention. The antecedent factors of behavioral intention included perceived behavioral control and injunctive norms, and two questions were asked about each of these factors (Table 1). The leading sentences for these items were “We ask your opinion for littering. Choose your opinion.” The above items were measured using a five-point scale, ranging from “1: Disagree” to “5: Agree.” Due to the limitation of the length of the questionnaire to discourage superficial responses, we set only two items as these were well-established [38,39,40] and not the main focus of the following analysis.

Table 1 Items of goal intention, behavioral intention, and related factors, and their mean scores
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The items for behaviors and implementation intention were originally prepared. A larger number of questions than the variables of goal intention and behavioral intention were prepared owing to the exploration of newly developed items. Particularly, behavior and implementation intention are anti-environmental; thus, questions should be carefully designed to reduce biased responses toward the socially desirable. Seven questions on implementation intention were provided (Table 2). For measuring implementation intention, we asked the following: “Imagine the situations that you have trash (e.g., food and drink, containers and packaging, small wrappers and bags, etc.) when you stay out. What would you feel when placed in the following situation?” The items were measured using a five-point scale, ranging from “1: Disagree” to “5: Agree.”

Table 2 Items of implementation intention, and their mean scores
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Thirteen questions on behavior were provided (Table 3). The leading sentence for behavior was as follows: “We ask your experiences during your daily outings.” In the study by Nagano [41], behavior was measured using a five-point scale: “1: Never,” “2: Once,” “3: Sometimes,” “4: Often,” and “5: Frequently.” When calculating Cronbach’s α coefficient to confirm the credibility of each scale, the scales were found to be valid (αs ≧ 0.83).

Table 3 Items of littering behavior, and their mean scores
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Questions on demographics are presented in Table 4. The entire questionnaire is contained in the Supplementary Material.

Table 4 Simple tabulation on demographic variables
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Analytical method

A simple tabulation was initially performed to observe the characteristics of each question regarding implementation intention and behavior. Then, a factor analysis was performed on implementation intention and behavior to approximate the validity of the scale. Thereafter, an improved SSBC model specific to littering behavior was investigated. Finally, demographics and other items were incorporated into the improved model generated to investigate a model that can predict littering behavior. R Studio 2022.02.3 + 492 was used in the analysis.

Results

Table 4 presents a simple tabulation of the demographics. The mean values and standard deviation of each scale for goal intention, behavioral intention, and antecedent factors are presented in Table 1. The values relating to implementation intention are indicated in Table 2, and those relating to behavior are presented in Table 3. Cross tabulations of the frequency of behaviors by age groups are shown in Tables S1–S13 in Supplementary Material.

Confirming the validity of measurements of littering behavior/implementation intention

Before investigating the model of littering behavior, we assessed whether the measured implementation intention and behavior had a bias toward socially desirable intentions and behaviors by looking at the frequency distribution. Concerning implementation intention, for items “I probably wouldn’t try to pick up small pieces of plastic that have been blown away by the wind,” and “When there are lots of weeds growing in a park or at the riverside, it’s easy to throw away my garbage in there,” the ratio of answers that were not pro-environmental increased (Fig. 2). This indicated that the implementation intention to litter arises to a certain degree in situations where there is litter in the area or if the litter one creates is hidden. By contrast, implementation intention aroused little with regard to the following items: “When there are no other people around, I feel inclined to (leave my garbage outside) just throw my garbage away,” and “When there is no garbage can around, I just want to leave my garbage (where I am and move on) on the spot” (Fig. 2).

Fig. 2
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Frequency distribution of implementation intention

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Concerning littering behavior, overall, few respondents admitted to littering; however, almost 40% responded “Once” to “I have put my own garbage into a shop’s garbage can and left,” and “When there was no garbage can nearby, I have left my food’s wrapping where I was eating it and went away” (Fig. 3). Regarding “The plastic bag I had in my bag or pockets was blown away by the wind, but I didn't try to pick it up,” “The wrapping of sweets that I put into my bag, disappeared before I knew it, but I haven't searched for it/them,” and “After going outside, the garbage, that I intended to throw away properly, just has disappeared,” about half of the respondents mentioned experiencing this once (Fig. 3).

Fig. 3
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Frequency distribution of littering behavior

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Creating a scale for littering behavior/implementation intention

When performing a factor analysis of questions relating to implementation intention, a single factor was confirmed (Table 5). When performing a factor analysis of questions relating to behavior, there were two factors: “littering in inappropriate places” (10 items) and “failing to actively retrieve litter” (3 items) (Table 6).

Table 5 Result of factor analysis on implementation intention
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Table 6 Result of factor analysis on littering behavior
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Model verification

Before verifying the model, the correlation coefficient between the scales was calculated (Table 7). A moderate positive correlation was observed between goal intention and behavioral intention, and a negative correlation was observed between behavioral intention and implementation intention and behavior. A stronger correlation was observed between implementation intention and behavioral intention with regard to behaviors. Moderate positive correlations were observed between goal intention and awareness of negative consequences, awareness of personal responsibility, and personal norms as well as among behavioral intention, perceived behavioral control, and injunctive norms.

Table 7 Correlations between factors
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A path analysis using structural equation modeling (SEM) was performed using the items measured in the questionnaire to develop a model to predict littering behavior. The initial model is illustrated in Fig. 1. The results of goodness fit indicated the following: [χ2(515) = 8107.72, p < 0.001, GFI = 0.89, AGFI = 0.87, CFI = 0.95, TLI = 0.94, RMSEA = 0.06, and SRMR = 0.04]. Considering that the scores of GFI, AGFI, CFI, and TLI are valid if they are above 0.9 to indicate good fitness [42,43,44], CFI and TLI met the criteria. GFI and AGFI scored below 0.9. In terms of RMSEA and SRMR, values less than 0.05 indicate a good fit and values less than 0.1 are considered acceptable. The results indicated that SRMR demonstrated a good fit, but RMSEA slightly exceeded 0.05, indicating an acceptable fit. Therefore, the goodness of fit in the initial model was generally satisfactory.

However, the model was modified due to two reasons. The first reason was the scale of the behavior. A confirmatory factor analysis for the behavioral items indicated a two-factor structure (Table 6). They were labeled “littering in inappropriate places” (Behavior 1) and “failing to actively retrieve litter” (Behavior 2). The behavioral scale of the model was modified from one to two factors. The second reason was the improvement of the model. The SEM calculation suggested a modified model demonstrating a better fit based on an amended index and the Wald test. However, behaviors were fixed to the terminal of the paths because the aim of the analysis was to predict behaviors. The final modified model was obtained shown in Fig. 4 [χ2(508) = 5895.68, p < 0.001, GFI = 0.92, AGFI = 0.91, CFI = 0.96, TLI = 0.96, RMSEA = 0.05, and SRMR = 0.04]. GFI, AGFI, CFI, and TLI exceeded 0.9. RMSEA and SRMR were below 0.05. Therefore, all indices indicated that the model fit was valid.

Fig. 4
figure 4

Improved model of littering behavior

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The results of the improved model revealed that when comparing the impact of behavioral intention and implementation intention on behavior, for both behavioral factors, the path coefficient from implementation intention was greater than that from behavioral intention. Furthermore, the path coefficients for behavioral intention and implementation intention, as well as behavioral intention and behavior, were both negative. In addition, while the path from behavioral intention was significant with regard to “littering in inappropriate places” (Behavior 1), the path from behavioral intention was not significant with regard to “failing to actively retrieve litter” (Behavior 2). This indicates that implementation intention had a greater effect on predicting behavior than behavioral intention. These results suggest that the assumed association of the characteristics of littering behavior could be expressed as a model. In other words, individuals who possess stronger pro-environmental behavioral intentions are less inclined to develop an implementation intention that would permit littering. However, if the situation allows for littering, the implementation intention to allow littering can nonetheless arise, thereby increasing implementation intention and ultimately resulting in littering behavior.

The effects of demographics

Before examining the effects of demographics on the improved model, the demographic items to be included in the model were narrowed down. The three intentions and two behaviors were set as the dependent variables, and demographics were set as the independent variables in a stepwise multiple regression analysis (Table 8). The variables that had no effect among the three intentions and two behaviors were removed from the model in advance.

Table 8 Results of multiple regressions analysis about the effects of demographics
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A path analysis using SEM was performed, including demographic items. The variables included in the initial model were identical to those depicted in Fig. 4, with the addition of paths connecting the three intentions and two behaviors to demographic items that exhibited significance in the multiple regression analysis. The results of goodness of fit indicated the following: [χ2(892) = 7471.57, p < 0.001, GFI = 0.90, AGFI = 0.88, CFI = 0.95, TLI = 0.95, RMSEA = 0.04, SRMR = 0.06]. GFI, CFI, and TLI exceeded 0.9, indicating the model’s validity. However, AGFI was below 0.9. RMSEA was below 0.05, indicating good fit, while SRMR was above 0.05 but less than 0.1, indicating an acceptable fit. The overall goodness of fit in the initial model was found to be sufficient. Furthermore, the paths in the initial model that were not significant were removed, and the model was modified based on an amended index and the Wald-test suggested SEM calculation. However, behaviors were fixed to the terminal of the paths. The results indicated that the goodness of fit indicators were slightly improved (Fig. 5). In the results, the path coefficient from demographics to each intention and behavior was mostly below 0.1, suggesting that littering was more common for all the intentions. The paths with relatively large coefficients were as follows: the paths from age and gender to the three intentions and behaviors indicated that men tended to litter more than women and that the frequency of the behavior increased among lower age groups. The path from smoking to littering behavior in inappropriate places indicated that littering in inappropriate places was more common among those with a history of smoking.

Fig. 5
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Model of littering behavior after adding demographic variables

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Discussion

This study investigated a model for explaining the processes leading up to littering behavior to obtain foundational data for proposing measures to inhibit littering around rivers, a reason for marine litter. Referring to the SSBC framework initiated by Bamberg [12, 20], implementation intention was posited as anteceding behavior. A model was proposed that assumed a negative or no association between positive behavioral intention and negative implementation intention in specific contexts and between behavioral intention and littering behavior. The results of the analysis revealed that implementation intention was the strongest direct determinant of littering behavior, while behavioral intention had a negative association with implementation intention. In addition, behavioral intention was negatively associated with one behavior and was found no association with the other behavior. In other words, individuals who possess an intention not to litter might still exhibit a willingness to tolerate littering and participate in littering behaviors themselves, particularly in situations where there is dense vegetation, an absence of nearby garbage bins, or where littering is not met with disapproval from their surroundings.

Nonetheless, this study still has limitations that require further exploration. There is a conceptual resemblance between implementation intention and other psychological factors. Depending on the definition, implementation intention is a combination of factors of intent with situational elements relating to when, where, and how. Situational norm is one element aroused in specific situations [14]. Situational norm includes, for example, the implicit norm of being silent in libraries and similar locations, causing behavior such as unconsciously lowering one’s speaking voice. Implementation intention has a similar function to situational norms insofar as they are the determinant that either permits or restricts behaviors in specific situations rather than in general terms. This also helps to understand the descriptive norm wherein many people will litter in areas with already a lot of litter. Further research is required to refine the concept and scales used for implementation intention and organize factors that underlie implementation intention.

In addition, this study did not include the descriptive norm in the model. Littering occurs in places with scattered trash, giving a notion of the descriptive norm that many people litter [5]. Further investigation is required to understand whether the descriptive norm can be a component of behavioral intention or implementation intention, differentiating the situational norm.

Despite the limitations mentioned above, this study has produced valuable findings. Littering behavior is a socially undesirable behavior. Therefore, while there was the possibility of bias when trying to understand this issue using a questionnaire, the measurements did not lean toward socially desirable responses.

Furthermore, this study serves as a bridge between studies using observation data and those examining psychological mechanisms. Existing field studies on littering have attempted to measure observable behaviors and determine situational and contextual factors, which contribute to [34, 45] or reduce littering [46]. Moreover, theories on preventing or promoting littering behaviors have been developed, such as broken window [47] and social norms [5]. Nevertheless, it has been difficult to capture the psychological construal because of the concern for the biased responses when measuring the variables in questionnaires. This study has attempted to overcome the limitations and propose a model leading to littering, thereby connecting the existing findings in the field studies and psychological model developed in the pro-environmental studies. The SSBC framework is well-suited to bridge the outcomes of field studies and explain psychological mechanisms. It assumes implementation intention triggered in a specific context, which is crucial for understanding littering behavior. Indeed, our results are consistent with the findings in the studies on field setting, indicating that behavior is impacted by surrounding factors, just as disorderly behavior is fostered in areas where broken windows and graffiti are left unchecked. These results are in line with the findings that an environment characterized by disorder tends to invite more disorder [48] and that the quality of the geographical physical environment impacts littering [49]. All these findings collectively suggest that the application of SSBC has successfully illustrated a model that aligns with empirical observations in real-life settings, despite the data being collected through a questionnaire survey.

This model will be useful when intervening in measures against littering. A common littering countermeasure is placing a warning to raise people’s awareness. People who habitually litter are told not to litter, for example. While such initiatives are undeniably important, the insights from this study suggest that they alone are insufficient. In other words, attempting to curb littering behavior solely by appealing to an individual's conscience or moral values is likely to have limited efficacy. It is imperative to generate situations that naturally discourage littering, as this approach is vital for implementing effective interventions to control littering [50]. Focus must be placed on situational factors. Surrounding factors that promote littering should be detected. For instance, these may include places having unmanaged overflowing garbage cans, places where other people's presence is not felt, and places where sanitation is poorly maintained [5].

Conventional pro-environmental behavior models focus on environmentally friendly behaviors. In the current paper, an anti-environmental model for littering was constructed. This study sought to understand negative behaviors in the environment using a questionnaire and clarify the processes leading up to littering behavior. Even if individuals intend to behave in an environmentally conscious manner, littering behavior is triggered by accompanying implementation intention that is not environmentally conscious once they are in a situation that allows for littering. Littering is an issue seen in many countries and regions. A start can be made toward controlling littering by understanding situations in which people end up littering. Further research should be conducted, referring this model, to verify whether creating specific situations that inhibit the feeling to allow littering can lead to a restraint on littering.

Conclusion

This study aimed to develop a behavioral model specifically focused on littering within the context of existing pro-environmental behavior models. The objective was to devise efficient and effective intervention measures to prevent littering. This study applied the SSBC framework to construct a model of the psychological process leading to littering behavior. This approach helped identify factors that should be considered for effective interventions in the field to curb littering. Survey results revealed that even individuals with pro-environmental behavioral intentions engaged in littering behaviors when dominated by anti-environmental implementation intentions in situations conducive to, encouraging, or tempting littering. The study findings help explain a gap between pro-environmental behavioral intentions and anti-environmental implementation intentions/behaviors. This study successfully captured the socially undesirable behaviors and implementation intentions, potentially prone to bias in the questionnaire survey. Finally, the model developed in this study and the results bridge the gap between field observational research and the understanding of psychological processes, potentially serving as a valuable resource for launching interventions to curb littering.