Next Article in Journal
How Do Transportation Influencing Factors Affect Air Pollutants from Vehicles in China? Evidence from Threshold Effect
Previous Article in Journal
Slope Instability Analysis in Permafrost Regions by Shear Strength Parameters and Numerical Simulation
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

What Influences Home Gardeners’ Food Waste Composting Intention in High-Rise Buildings in Dhaka Megacity, Bangladesh? An Integrated Model of TPB and DMP

1
Graduate School of Business, Universiti Sains Malaysia (USM), Penang 11800, Malaysia
2
Department of Software Engineering, Daffodil International University, Dhaka 1207, Bangladesh
3
Brac Business School, Brac University, Dhaka 1212, Bangladesh
4
Labuan Faculty of International Finance, Universiti Malaysia Sabah, Labuan 87000, Malaysia
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(15), 9400; https://doi.org/10.3390/su14159400
Submission received: 21 May 2022 / Revised: 28 June 2022 / Accepted: 1 July 2022 / Published: 1 August 2022
(This article belongs to the Section Waste and Recycling)

Abstract

:
Composting is a sustainable way of transforming organic waste into valuable organic fertilizers which have the potential to act as soil conditioners by controlling various biological processes. The prime objective of the current study was to determine the influencing factors behind the intent of home food waste composting, by employing the combined model of Theory of Planned Behavior (TPB) and Dualistic Passion Model (DMP). The combined model showed a higher predictive ability in comparison to the individual TPB model. The fit statistic of the integrated model was deemed good, and 65% of the variance for home composting intention was explained. Using a face-to-face questionnaire survey, a total of 203 valid responses were gathered from home gardeners and tested via a unique two-step methodology: the PLS-SEM and the artificial neural network (ANN). The results revealed that the composting intention can be significantly influenced by attitude, subjective norms, and perceived behavioral control. The study also confirmed the positive effect of harmonious passion and the negative effect of obsessive passion on the intention of food waste composting. Furthermore, the hybrid method produced more reliable results because HP was found to be the most important variable in both ANN and PLS-SEM results, while PBC was observed to be the second most important variable in ANN and the fourth most important in PLS-SEM. The results of the current study not only highlight the importance of passion in determining food waste composting intention in Dhaka, Bangladesh, but also provide helpful information for designing effective, sustainable tactics for encouraging residents to compost food waste at home.

1. Introduction

Municipal solid waste (MSW) management has become a top concern of local governments in developing nations due to its environmental and economic consequences. The majority of municipal solid waste is generated by urban residents, in turn resulting in substantial management expenses for local governments [1]. As a result of growing urbanization and population expansion, the global quantity of waste generated is projected to increase from 2 billion tons to 3.5 billion tons over the next 30 years [2,3]. MSW is generated from different sectors, including households, commercial or business, and industry [4]. The volume of waste generated is increasing in metropolitan areas more than in small communities. For instance, the daily waste generation in cities ranges from 0.47 to 0.5 kg per capita [5].
On the other hand, the rate of waste production is almost half in small communities [6]. In Bangladesh, households and commercial or industrial sectors are the two major sources of organic waste. Approximately 75% of total solid waste generated comes from households, and the remaining 25% from commercial areas [7]. Approximately 4.86 million tons of solid waste is generated annually in Bangladesh’s metropolitan regions, in which organic waste accounts for 75% to 85% of the total [6]. Despite its potential for reuse, only a small percentage of the total organic waste is composted into organic fertilizers and recycled [8].
Household solid waste accounts for approximately 90% of the total municipal solid waste streams, and 80% to 90% of this waste is organic [5]. Households are responsible for the majority of food waste generation during consumption [9]. Food waste accounts for 50% of all municipal solid waste in Europe, and 55% of all municipal waste in both developing and developed countries [10]. Bangladesh has an abysmal record, with food waste accounting for 68.3–81.1% of total municipal solid waste [11]. The vast amount of food waste being generated has resulted in adverse consequences and hazards to human health and societal development. For instance, a huge part of the planet’s resources are absorbed by the storage and disposal of waste. When the balance is upset, groundwater, soil, and air are polluted, giving rise to excess flies, mosquitoes, and detrimental bacteria [12]. In addition, if food waste is not treated properly, it releases methane, which is 84 times more destructive than the carbon dioxide pollution caused by transportation. Additionally, the accumulation of food waste in landfills can result in big explosions because of the accumulated methane gases trapped inside [13].
Food waste, or kitchen waste, is a valuable resource that can be converted into biofertilizer and biofuel using advanced biotechnological methods in order to achieve sustainable development [14]. Composting is widely used to treat kitchen waste [15,16] due to its high moisture content and putrefaction capability [17]. Compost is the end product of the aerobic breakdown of organic substances [18]. It is considered a soil conditioner and is widely used as a natural fertilizer in agriculture [6]. Home composting includes the biodegradation of municipal organic waste, including food or kitchen waste [19]. In terms of sustainable waste management practices, home composting is considered to be a productive option for handling kitchen waste at the source. It has numerous benefits, such as creating valuable products capable of enhancing soil structure and fertility [20,21,22,23], as well as the joy of experimentation and an environmentally friendly lifestyle. Home composting can potentially be a feasible option for managing the biological fraction of municipal waste, especially in developing countries, due to its simplicity and quick setup process. Furthermore, this process is cost-effective in comparison to other recycling alternatives, which need sophisticated resources for both operation and maintenance. Therefore, composting is considered to be a more sustainable, efficient method of food recycling because it is less harmful to the environment and has lower economic costs [14]. Additionally, prior studies have stated that composting is a more eco-friendly recycling option than any other food or organic waste recycling method such as landfills or incineration [24,25]. Home composting also makes it easier for the individual homeowner to recycle organic waste in a sustainable way [21]. An exploratory study by Fernando [26] concluded that the reasons for people’s participation in home composting include it being a convenient way to dispose of food waste, using it as an organic fertilizer in home gardening, and also being concerned about the environment, good health, and overall economic benefits.
However, composting at home is not considered an option for overall MOWs [21]; this method can only be considered the best household-level waste management strategy [27,28]. Despite its significance in organic waste management, it is not yet widely used around the world. In the South Asian region, only 16% of the total MSW generated is composted, while the remaining 75% is deposited in open landfills, according to the MSWM method [29]. Therefore, it is essential to first identify the underlying factors that influence food waste composting at the household level.
Attaining an integrated MSW management begins with identifying people’s attitudes, social norms, and behavioral intentions, and supporting of the solid waste management structure by the local government and commercial sectors [30]. Attempts in poorer nations, including Bangladesh, to achieve an effective solid waste management system have been highly focused on the 3R strategy, effective collection methods, and source separation [5]. Despite the tremendous social, economic, and environmental benefits of these programs, social factors such as a lack of understanding and poor participation in recycling initiatives have hampered their implementation [31].
This study is among the first to examine the underlying factors which govern the intentions of food waste composting in Bangladesh by considering the most significant and leading role of home gardeners. Three major contributions are envisioned from the outcomes of this study. Firstly, this research examined the food waste composting intention of Bangladeshi home gardeners. Next, the combined model of TPB and DMP was employed as the theoretical framework for empirical analysis for confirming the feasibility and validity of the estimation outcomes. Finally, this study reached comprehensive findings and proposes strong policy implications by considering the intentions of the target group. The willingness of residents to take part in composting activity has been examined previously in the context of composting behavior. Loan et al. [32] measured composting behavior within sustainable MSW management in developing nations. In order to measure the intention of green composting, Mamun et al. [33] examined the perceived benefits, normative beliefs and the required start-up resources in the Malaysian context. Dwinadine & Dewi [34] examined the intention to use waste for composting and found significant factors viz. perceived value, trust, and knowledge.
The current study aims to fill the knowledge gap in waste recycling literature and attempts to answer the following research questions: (i) what are the key drivers behind the intention to compost food waste? and (ii) how important are the factors in predicting intention to compost food waste? This study uses a two-tier analytical approach by combining structural equation modelling (SEM) to explore the first question and the artificial neural network model (ANN) to evaluate the second question, as intended by prior researchers [30,31,32,33]. The second research question was evaluated using ANN to consider non-compensatory and non-linear relationships [35,36]. Furthermore, this systematic hybrid technique presents a detailed insight into the subject and simultaneously, the benefits of a single method offset the drawbacks of another method. Thus, to support theoretical developments and practical approaches, this research aims to develop a model that exhibits the home gardener’s intention to compost food waste.

2. Literature Review

2.1. Theory of Planned Behavior

Theory of planned behavior (TPB) was formed by including Perceived Behavioral Control from the reasoned action theory. TPB is extensively employed to predict various types of human behavior [37] and is considered one of the most popular theories for investigating social and psychological behaviors [38]. TPB suggests that people act rationally by implicitly or explicitly considering the outcomes of their actions. This theory postulates three major factors viz. attitude (ATT), subjective norm (SN) and perceived behavioral control (PBC), to predict behavioral intention (BI) [39]. Attitude indicates a person’s positive or negative judgement of a particular behavior [38]. Subjective norm is described as the understanding of social force as a reason for a particular behavior [40]. PBC refers to how people perceive the ease or difficulty of a particular behavior [41]. Thus, the theory posits that a person with more desired ATT, SN, and a better PBC is expected to exhibit a stronger BI [38].
In addition, TPB has been integrated and expanded by numerous other factors. Wang et al. [42] conducted a China-based study on the impact of environmental awareness, norms, convenience of recycling, income, cost of recycling and attitudes on the consumers’ intention to recycle. The effect of disclosure of information, awareness of consequences and the attribution of responsibility were analyzed in another study by Wang et al. [43]. In the area of food waste, Mak et al. [44] extended the TPB theory to include economic incentives, logistics and managerial incentives, administrative incentives, and business support. Abbasi et al. [45] employed TPB factors to explore the intention to revisit a destination.
The effectiveness of TPB, in terms of composting food waste, was preferred as the theoretical grounds for this research. Although TPB has been extensively studied in the past, the predictive power of this theory has been criticized as being insufficient and does not adequately explain a specific environmental behavior [46,47]. In order to improve the predictive power of the model, researchers have stated that further variables need to be included in the model, based on the specific research context and background [42,48]. Similarly, Ajzen [49] posited that “TPB is an open theory and additional variables can be added if they are able to capture a significant fraction of the variance in behavioral intention”. Therefore, this study aims to understand the intention of food waste composting by including the DMP (Dualistic model of passion-harmonious passion and obsessive passion).

2.2. Dualistic Model of Passion

Vallerand and his colleagues [50,51,52] developed a passion model for the inherent dualism of passion. Similar to the self-determination theory proposed by Deci & Ryan [53], DMP states that humans are motivated to examine their surroundings in order to grow. Vallerand et al. [51] characterized passion as a strong tendency towards a self-defined activity that one prefers, deems necessary, and in which one invests time and energy on a regular basis. Moreover, self-determination theory, as well as other research carried out, found that elements from the environment can either be controlled or be autonomously internalized [54,55]. Therefore, it can be said that DMP divides passion into obsessive and harmonious, based on how the passionate action has been integrated into an individual’s self-identity.
Harmonious passion appears from the autonomous incorporation of an event in the self which arises when individuals voluntarily acknowledge the activity and prefer to take part with a passionate interest and without any contingency [56]. Additionally, willingly engaging in the activity creates a motivating force and generates a sense of will and personal validation to continue the activity. In harmonious passion, activity involves an important but not dominating place within one’s personality. Consequently, the activity is under the individual’s control and is harmonious with other crucial aspects of life [57,58]. Obsessive passion (OP) is an unmanageable urge to engage in an activity that one loves [51,59]. The person feels a pressure to continue pursuing the activity. Additionally, people who have an OP may have an irrepressible need to participate in the activity they value and appreciate. The passion for the activity controls the person’s action. Thus, engagement in a passionate activity brings rigid persistence in the activity. This rigid attachment can lead to other matters in the individual’s life being neglected (such as when one should be doing something else), creating tension and conflict and thus leading to a poor integrative experience in task accomplishment and hence negative emotional experiences [60].
Afsar et al. [61] recommended further research on the connection among the DMP and within the context of pro-environmental behavior. Li et al. [62] studied harmonious passion to predict organizational-level environmental behaviors of employees. Akhshik et al. [63] applied DMP and mindfulness to tourism. We have expanded our search by including SLR (see Supplementary Table S1 for details); to the best of the authors’ knowledge, neither HP nor OP have yet been tested along with TPB to predict the intention to compost food waste. Thus, the current approach would fill the gap of limited knowledge on the application of DMP in food waste recycling research, though limited empirical studies hamper investigating more robust factors behind food waste recycling and composting at the individual level.
Food waste is a major problem worldwide. Especially in developing countries like Bangladesh, food waste disposal is undervalued and not well managed due to poor management by local communities. Bangladesh has an unfavorable record, with food waste accounting for 68.3–81.1% contain from municipal solid waste [64]. A significant amount of food waste does not generate economic value due to immature waste recycling practices [65,66]. Lack of government intervention and inappropriate dumping make food waste management a serious challenge for Bangladesh.
In 2014, domestic waste generation in Bangladesh amounted to 23,688 tons/day for a total of 41.94 million metropolitan population and an average of 0.56 kg/capita/day due to the growth of metropolitan areas and population increase. This excessive amount of waste is mainly generated by high-rise dwellers [65]. Previous research examined 2736 buildings in a large area of Dhaka city and found that 36.4% of the buildings have a roof garden where residents were involved in gardening at home [64]. The literature review (see Supplementary Table S1 for details) shows that previous researchers thoroughly examined residents in a general way rather than distinguishing them based on their hobbies or activities. However, only a limited number of empirical studies have investigated home gardeners’ intention to compost or recycle food waste. Therefore, the current study focuses solely on home gardeners, as they use fertilizer for growing their plants and vegetables, though it is mentioned in previous studies that composting can minimize food waste issues at household level. Therefore, it is imperative to examine individuals who engage in home gardening, such as home gardeners.

2.3. Hypotheses Development

The following sections discuss the hypotheses examining the behavioral aspects of home gardeners with regard to food waste composting in Dhaka, Bangladesh. The overall model emphasized on interactions between different predictors, as illustrated in Figure 1.

2.4. Attitude

TPB, postulated by Ajzen [49], is the most commonly used theory to predict human behavior. The model proposed in this theory includes the three important constructs of attitude, subjective norms and perceived behavioral control. These three factors are determinants of predicting the individual’s behavioral intention. One of the variables, attitude, has long been acknowledged as a major element of intention and behavior and has a significant impact on it. In the study of the green composting adoption, Mamun et al. [33] discovered that attitude was one of the most important elements in predicting composting intention. Onel & Mukherjee [66] observed in their study that attitude played a major role in the domestic waste recycling intentions. A study by Lam et al. [67] identified that attitude was a potential factor of environmentally conscious behavior. Additionally, Zhang et al. [68] and Stoeva & Alriksson [69] observed the positive relation between attitude and an individual’s waste recycling intention. Thus, having a favourable attitude towards certain behavior improves the behavioral intention. Hence, the following hypothesis is proposed.
Hypothesis 1.
Attitude positively affects the intention of composting food waste at home.

2.5. Subjective Norm

Subjective norm is concerned with everything surrounding an individual, such as social norms, social pressure, cultural norms, or other group beliefs [70]. This variable is associated with social pressure, which shapes an individual’s behavior in specific situations where they may execute certain behavior with their social peers’ consent [71]. Subjective norms are used to understand how social norms affect municipal solid waste minimization, as they take into account general public hurdles such as a lack of awareness, motivation, and influences [72]. Ajzen [49] found that significant others were the primary source of social influence for an individual; in other words, their family, friends, neighbors and the community. The current study also states the relevance of subjective norms and their significant connection with the intention of food waste composting. According to Yuan et al. [73], social pressures from significant others might either motivate or oppose a person’s aim to recycle kitchen waste. The findings of Ramayah et al. [74] stated that subjective norm was a major contributor to recycling intention in areas of poor waste management. Numerous earlier empirical studies have observed that an individual’s recycling intention was significantly influenced by their social norms, which were fundamentally derived from others’ attitudes to waste recycling intention [75,76,77]. Hence, the subjective norm directly influences and increases the food waste composting intention among home gardeners in mega cities when the social norm respectively reveals certain role models and inspirations towards waste separation for composting. Therefore, the following hypothesis can be posited.
Hypothesis 2.
Subjective norm positively affects the intention of food waste composting at home.

2.6. Perceived Behavioral Control

PBC, the third predictor of the TPB framework, refers to the perceived ease or complexity of carrying out certain behavior. PBC functions as a twofold concept and includes views about one’s capacity to carry out an activity with the accessibility of the required resources and opportunities to perform the behavior, the beliefs about an individual’s abilities to execute an action and the availability of essential resources and prospects to engage in the behavior [49]. Other elements are money, time, knowledge, and skills, which can intensely affect one’s control over the behavior [78]. Prior research suggests a strong and positive correlation between perceived behavioral control and pro-environmental intention. The study by Yuan et al. [73] validated the significant effect of PBC on the recycling intention of kitchen waste. The findings of their study were in line with the findings of Wang et al. [79], who demonstrated that PBC was the strongest predictor to influence the intention to recycle household waste of Chinese residents. The implication of these studies was that people who had a good command over their self-actions and who had access to resources for implementation were prone to certain behaviors. With these implications, the following hypothesis is proposed.
Hypothesis 3.
Perceived behavioral control positively affects the intention of food waste composting at home.

2.7. Harmonious Passion

Vallerand et al. [51], proposed in their dualistic model of passion (DMP), that an individual’s intention or behavior should be analyzed by distinguishing two different passions—harmonious passion (HP) and obsessive passion (OP). HP and OP have been used in past studies to understand the home gardener’s composting intention. Harmonious passion has significant implications for environmentally conscious behavior [63]. HP is positively related to alertness, focus, the motion during activity, as well as a positive effect, quality relationships, and psychological well-being. Individuals with HP generally experience many positive effects [80]. This positive effect mediated between harmonious passion and the student’s intention to drop out [58]. A harmoniously passionate individual experiences positive emotions, which is considered to be the most important factor in energy-saving intentions [81]. Research on environmental behavior has indicated that positive emotions may be associated with more environmental behavior [82]. It has also been found that HP was positively connected with the endorsement of mainstream behaviors [83]. Therefore, for this study, the following relationship can be hypothesized.
Hypothesis 4.
Harmonious passion positively affects food waste composting intention at home.

2.8. Obsessive Passion

According to Junot et al. [80], OP is linked to negative emotions, a connection to nature, and environmental behaviors. Generally speaking, negative emotions cause self-centeredness and self-centred behavior. People become egotistical and cut themselves off from others and the rest of the world when harbouring negative emotions [82,84]. Individuals experiencing unpleasant emotions are more likely to concentrate on urgent problem-solving methods because they are less likely to be interested in matters that have a minor direct influence on them, such as environmental issues. A study by Akhshik et al. [63] examined the link between OP and pro-environmental behavior, and suggested that visitors who have OP were less likely to demonstrate pro-environmental behavior (PEB). OP has also been strongly associated with the intention to engage in both mainstream and radical behaviors [83]. Thus, the following hypothesis can be formulated.
Hypothesis 5.
Obsessive passion negatively affects food waste composting intention at home.

3. Methods

3.1. Measures of Constructs

The measurement tool for the current study was based on the components of research models of previous research. All TPB items were adapted from Rastegari Kopaei et al. [85] and the DMP items from Akhshik et al. [63], respectively. All constructs were measured using a 7-point Likert scale ranging from strongly disagree = 1 to strongly agree = 7. Since data collection was carried out in Dhaka city, the questionnaire was therefore translated to Bengali. The questionnaire was evaluated by a panel of five experts prior to the data collection process in order to ensure that it met the requirements of face and content validity. Furthermore, a pilot study was also carried out with 30 sample respondents, whereby a composite reliability of all the variables was found to be greater than 0.708. The questionnaire section comprised of 21 items linked to ATT, SN, PBC, HP, OP and composting intention. To follow the purposive sampling method, this study also included three screening questions in the survey questionnaire in order to identify the appropriate respondents for this study. For example, (1) Do you have composting knowledge? (2) Are you involved in rooftop/home gardening? (3) Are you involved in throwing/discarding food waste into the kitchen bin?
Respondents were required to respond to these questions first before they proceeded to answer the main questionnaire items. If the screening questions were not relevant to them, they were requested to stop their response as per the instructions given in the questionnaire. Table 1 highlights the list of constructs and measurement items.

3.2. Study Area & Respondents

Dhaka, the capital city of Bangladesh, was selected as the study location due to its large population (19.5 million), record amounts of daily waste production causing serious issues, and the huge municipal waste management costs incurred by the government as a result [86]. Dhaka is also considered to be “one of the largest and most densely populated cities in the world”, with a density of 23,234 people per square kilometer within a total area of 300 square kilometers. Approximately 38% of Bangladesh’s urban population reside in Dhaka [87,88]. Geographically, this city is located at 23.8103° N latitude and 90.4125° E longitude. The average annual rainfall is 2077.2 mm and daily average temperature is 26.6 °C, fluctuating between 21.7 °C and 30.8 °C [6]. Dhaka City Corporation (DCC) area was used as a study area as the percentage of rooftop gardeners was promising in number [65]. The number of high-rise buildings in Dhaka city increased remarkably over the last decade. Therefore, the population of the city grew drastically with its vertical high-rise development [6]. Thus, it is essential to identify the residents of high-rise buildings who have alternative food and organic waste recycling techniques. This study focused primarily on exploring the residents’ (home gardeners) food waste composting intention in high-rise residential buildings in Dhaka. All home gardeners/rooftop gardeners were the target respondents for this study. Consequently, an empirical study that particularly concentrates on home gardeners could be important and beneficial to policy makers in order to develop the home gardeners’ food waste and composting recycling behavior.

3.3. Data Collection Procedure

A face-to-face questionnaire was used for conducting the survey. The study was conducted in three overpopulated areas of Dhaka city viz. Dhanmondi, Mirpur and Mohammadpur. Followed by a cross-sectional method, primary data was collected during the period of 15 March 2022 to 23 April 2022. Data collection was carried out in three steps. First, the apartments with rooftop gardens were identified through information gathered from the Dhaka City Corporation. Secondly, the questionnaires were circulated to the households through the apartment management office. Finally, out of a total of 500 questionnaires that were distributed, only 203 valid questionnaires (a 41% response rate) were ready for the final analysis.

3.4. Common Method Bias

This study used the Harman’s one-factor test and a Marker variable to measure the overall common method bias [89], since the same individual answered all the survey questions. In this test, the risk of bias from the common method was deemed high when a single factor was responsible for more than half of the variance [89,90,91]. The findings of this study confirmed that neither factor strongly dominated the explanation of the variance (the most significant factor reported for 20.92 percent of the variance). Furthermore, a marker variable was also included in the model which tested the correlations between the marker variable and all other variables of this study. The lack of correlations between the marker variables and other variables in the main model revealed that common variance was not an issue in this study.

3.5. PLS-SEM and ANN

PLS is considered a second-generation SEM instrument that is capable of conducting simultaneous analysis with multiple dependent constructs. In addition, PLS-SEM has a marked advantage over traditional regression-based techniques with more variations from the data, even when the model is complex and fails to meet linearity and normality assumptions [35,91,92,93]. Therefore, PLS-SEM was chosen to obtain the maximum explanation from the source. Once the significant hypothetical relationships were identified by using PLS-SEM, further estimation was done by using ANN, an artificial-intelligence-based system designed to function similarly to the human brain. In ANN, the algorithm works in feed-forward-back-propagation, which is a method where knowledge development is based on an iterative learning process by feed-forward and then compared to the actual output (back-propagation). PLS-SEM has been shown to be able to analyze linear hypotheses but unable to deal with nonlinear interactions, whereas ANN is better at capturing nonlinear relationships [35,91,92].

3.6. Data Analysis Strategy

Descriptive statistical analysis was performed to analyze the data using the statistics packages SPSS version 21.0 software and the correlation between constructs was analysed by employing PLS3.0 software. In order to obtain more robust results, this study used a two-stage hybrid PLS-SEM-ANN approach to capture the maximal explanation from the hypothesized relationships [91,92]. Finally, a bootstrapping technique (500 resamples) was used to assess the significance of the path [94,95].

4. Results

4.1. Descriptive Statistics

The demographic information of the respondents stated in Table 2 was retrieved from the descriptive analysis by performing SPSS. According to the data in Table 2, 16.2% of sample respondents (n = 33) were males and 170 (83.7%) were females. This bias was caused by targeted interviewees (women) who mostly resided at home and engaged in household activities, which was similar to a previous study performed by Nasrin et al. [96]. Age categorization of the respondents indicated that most belonged to the 41–50 age group. This statistical finding was similar to [96], where the majority of the rooftop gardeners in Bangladesh belong to the group of 41–50. Finally, the majority of the respondents highlighted that they were housewives and non-employed (73.3%, n = 149). The descriptive analysis also identified that most of the participants (43.8%) had a monthly family income of Tk 70,001–Tk 90,000 (see Table 2).

4.2. Convergent Validity

The reliability and convergent validity of measurement model were established by analysing the construct loading, composite reliability, and average variance extracted, according to a prior study [97]. Table 3 highlights that CRs were greater than 0.7 and each construct’s average variance extracted (AVE) was higher than 0.5, which means that the convergent validity of the measure of this study was established [98].

4.3. Discriminant Validity

The discriminant validity of the outer model was examined by calculating the square root of the AVE for each construct with the correlations among the variables [99]. As demonstrated in Table 4, the square root value of the AVE for each variable was greater than all associated construct correlations and discriminant validity of all scales was determined.

4.4. Structural Model

This study measured the hypotheses using a structural model after examining the validity of the outer model. The proposed model exhibited a R 2 value of 64.7% variance on the composting intention, which was greater than the minimum R2 value of 25% [100]. In addition to R 2 values, the present study also assessed the predictive relevance ( Q 2 ) by running a blindfolding technique. Table 5 shows the values of both R 2 and Q 2 . Values greater than zero were indicative that the model had overall predictive relevance [100,101]. Moreover, the current study’s findings demonstrated a satisfactory predictive relevance for the endogenous construct—composting intention (0.33), where the Q 2 values were more than zero. Additionally, this study also investigated the effect size ( f 2 ) to detect whether a specific exogenous construct had a substantial impact on an endogenous variable [102]. The results of the study revealed that the f 2 for the supported hypotheses was acceptable. Table 6 highlights the effect size for the hypothesized relationship.

4.5. Hypotheses Testing

To investigate the proposed hypotheses relationships, the path coefficients were assessed. As proposed by Hair et al. [103], the bootstrapping method was carried out on 5000 subsamples. Consequently, at 1% level of significance, all hypotheses were found to be significant. Specifically, HP (β = 0.382, p < 0.01) was found to have the highest path coefficient value, which contributed to the existence of HP in an individual’s intention in terms of food waste composting. ATT ranked as the second highest path coefficient (β = 0.274, p < 0.01), which explained that the existence of individual’s favorable attitude towards food waste composting. Furthermore, SN and PBC had the 3rd and 4th highest influence on CI, which was valued at β = 0.274, p < 0.01 and β = 0.241, p < 0.01, respectively, and showed the impact of social circle on the home gardener’s intention to compost food waste, whereas the perceived easiness of the individuals also makes them compost food waste. Finally, CI was observed to be negatively affected by OP (β = −0.127, p < 0.05). It can be said that each of the hypotheses were significant in the current study. The significant relationships were consistent with the findings of previous research studies and TPB’s proposal; nonetheless, the discrepancy is due to changes in path coefficients and their relative relevance. The hypothesis testing estimations and outcomes are presented in Figure 2 and Table 7.

4.6. Outputs of Neural Network Analysis

The Statistical Package for Social Science (SPSS) software version 24 was used to examine the ANN model for this study. The supported variables of the PLS-SEM analysis were used as input neurons in the model. As illustrated in Figure 3, there are five significant drivers of intention (ATT, SN, PBC, HP, and OP). Through the software, the total number of neurons in the hidden layer was created simultaneously. However, according to more recent studies, identifying an accurate number of neurons in the hidden layer is a continuing challenge [36]. In order to avoid the overfitting error that commonly occurs in the ANN model, a 10-fold cross-validation process was employed. Around 90% of the answers were used for learning and the remaining 10% for prediction [93,95] (see Figure 3). Table 8 highlights the mean and standard deviation of the root mean squared error (RMSE) values for both training (learning) and testing (predicting), whereby the possible lower limit of the RMSE is zero (0) with an unlimited upper limit. However, the closer the RMSE values are to zero (0), the greater the ANN model’s predictive capacity [104,105,106]. The results of this study demonstrated that the RMSE mean for training and testing was 0.392 and 0.355, respectively, highlighting that the neural network models seemed to be extremely reliable in capturing the linear/nonlinear relationships, coinciding with a prior study [92]. The RMSE mean values were reasonably small with negligible standard deviations both in the learning and prediction phase, and the ANN models seemed to have a higher level of accuracy when measuring the relationships, similar to those obtained in previous studies [36,92].

4.7. Ranking of Factors

A sensitivity analysis was calculated in the ANN model in order to rank the predictors based on their relative importance. Chong [106] stated that “the relative importance of the predictors is a measure the extent of the network model that predicted changes in the output values in relation to the changes in the values of independent variables.” The list of the normalized importance variables for the current study is shown in Table 9, wherein HP was the most important predictor of composting intention, followed by PBC, ATT, SN, and OP.

5. Discussion and Implications

In the current study, a comprehensive model for studying the behavioral intention of food waste composting was developed incorporating both traditional cognitive elements (ATT, SN, and PBC) and other elements related to the concept of pro-environmental behavior (HP and OP). The results obtained show that the model was capable of justifying more than half of the variance in intention to compost food waste, thus proving that the integrated model had a significant possibility of explaining food waste composting behaviors by home gardeners in Dhaka. Additionally, the results emphasized the importance of DMP in shaping the intention to compost food waste.
The current study examined attitude as a significant construct for composting intention. Previous studies have also shown that attitude had the greatest impact on an individual’s recycling intentions [107]. The findings of Ajzen and Fishbein [108] stated that recycling-specific attitudes were considered to be better predictors of recycling than general environmental attitudes. Considering the usefulness of composting, home gardeners have a positive attitude towards composting food waste. The present research is consistent with various areas of pro-environmental studies that emphasizes the importance of attitude towards recycling [109,110,111,112,113].
The results demonstrated the greatest effect on composting intention to the SN. This outcome is similar to those observed in previous studies [75,76,77]. Pressure on the individual for home-based composting from friends and family usually drives them to perform. For instance, when an individual cultivates organic food using organic fertilizer, it leads to better quality of vegetables and fruit [113], which in turn influences their society, neighbours and friends. The current study was conducted in the metropolitan city of Dhaka, where residents want organic food and household waste management is a big issue among high-rise buildings. The waste collection does not always collect the waste on time. To avoid these circumstances, residents tend to recycle their organic waste by composting. Thus, this alternative waste management technique will influence residents to have the intention to compost food waste.
The observations in the current study indicate that PBC positively impacts the intention to compost, which reflects the results of a cross-sectional study conducted earlier by Rastegari Kopaei et al. [85] in Iran. The result of PBC shows that the more information and practicality an individual has about the phases of home composting, the more likely they are to compost. PBC can therefore be considered as a non-volitional factor of composting intention. The results obtained in the current study indicated that home gardeners did not experience difficulties in purchasing equipment for composting. This result was similar to those obtained in prior research in environmental protection [75,114,115].
Furthermore, this study also observed a significant relationship between HP and composting intention. This implied that HP played a pivotal factor for home gardeners to freely accept this activity and choosing to engage in it without any contingency attached to it. Previous studies have proven that harmoniously passionate people were more likely to achieve pro-environmental behavior [63]. Thus, it can be stated that the results of the present study implied that HP is a crucial factor to home gardeners, which consequently had a profound impact on the home gardener’s subjective well-being and their composting intention.
The outcomes of this study also showed that OP negatively affected the composting intention of home gardeners. This was consistent to the results observed in earlier studies by [115]. Descriptive analysis of the current study indicated that almost 83% of the respondents were women. In developing countries, women play a key role in maintaining household affairs by performing daily household chores. If they can manage both household activities and composting activities, they may experience a rigid conformance to the activity. Furthermore, such rigid perseverance may result in clashes with other elements of their life when engaged in the passionate activity (such as when one should be doing chores or other personal activities).
Other household members may also perform composting during the weekend and in doing so disregard their family, thereby causing conflict between the passionate interests (composting and other life events). Thus, this could potentially cause negative influences on the intention to compost. Furthermore, OP is linked to negative emotions, which may lead to single-mindedness and self-centred behaviors. As a result, home gardeners may tend not to the prioritize food waste composting activity and rather concentrate on their personal activities.

5.1. Theoretical Implications

This study contributes to the body of existing knowledge in the field of IS, specifically waste management. This study has provided a comprehensive model that includes TPB factors and DMP while assessing the intentions behind food waste composting among home gardeners in Dhaka. The results of this study justified the home gardeners’ ATT, PBC, SN, HP and OP in food waste composting. Thus, the outcome of this research has theoretical implications. In particular, this research contributes to the waste management/recycling literature by offering a more comprehensive model that includes passion constructs namely, HP and OP. The inclusion of the DMP factors increased the explanatory power of the proposed framework, wherein 64.7% of the variance of composting intention could be explained, implying that both HP & OP are accurate factors of food waste composting intention. Hair et al. [94] recommended R 2 values of 0.25, 0.50, or 0.75 for independent constructs as weak, moderate, or significance coefficients of determination. This current study also enriches the waste management domain by serving as a starting point for other future studies. To the best of the authors’ knowledge, this study is among the first to prioritize the connection between DMP and home gardeners’ food waste composting in high-rise building in Dhaka, Bangladesh. Nonetheless, there have been a few studies conducted in the past examining DMP along with TPB factors in waste recycling studies.
Finally, the current study used a unique two-stage analysis method by incorporating SEM and ANN for the validation of the proposed framework and to highlight the most important elements affecting composting intention. This was achieved by examining the relative importance of each supported variable. This study makes a distinction between the PLS-SEM ranking and ANN ranking for the predictors of composting intention and found significant differences. The disparities noted could be because the two approaches used were distinct in nature, with SEM measuring linear relationships and ANN measuring nonlinear relationships between predictors and target variables. Previous studies have noted that a neural network analysis could provide higher prediction accuracies in comparison to the SEM approach [91,116]. However, this study did not ignore the validity and necessity of traditional statistical methods, which past research has suggested to be a solid foundation for IS adoption studies. This current study proposes reinterpreting the prior IS adoption studies by combining both linear and non-linear approaches, adding profundity to the present waste recycling literature.

5.2. Practical Implications

This study’s findings have important practical implications for NGOs and the government for motivating the community to carry out food waste recycling through composting at home. Because of the significant contribution of ATT, PBC and SN in composting intention, the respective agencies can provide enhanced composting training courses and coordinate lectures on performing composting at the household level with proper equipment. Training sessions can also be conducted which should not only be theoretical but also practical in nature [8]. Residents may also face many difficulties while composting, thus necessitating the need for advice. Free field classes can be arranged involving both parents and children. However, if some residents cannot attend the field classes, farm visits can be organized by local authorities and composting consultations can be conducted. Additionally, local organic fertilizer manufacturers can provide composting tools to interested households and in turn purchase the generated organic fertilizer from them. This would prove beneficial to both parties and motivate other non-gardeners to take up food waste composting at home. Government and educational institutions should also educate the younger generation about the effectiveness of the composting system and the appropriate environmental agencies can convey awareness through social media campaigns. These practices can help individuals to understand the significance of food waste management and environmental protection. Furthermore, they help individuals develop a satisfactory and positive attitude, perceived behavioral control and subjective norm towards food waste composting and recycling at home. These measures can assist in enhancing an individual’s daily food waste recycling abilities, as well as make them aware of their capacity, duty, and responsibility to protect the ecosystem and also change other individuals with low psychological motivation.
Since the home gardener’s HP positively influences home composting intentions, the results may prompt the relevant environmental authorities to design a cost-effective and environmentally friendly composting infrastructure for residents. Therefore, government agencies or local communities must focus on stimulating and inducing positive emotions in households to show pro-environmental behavior and adjust sustainability. Training partners can design the composting training program to include fun activities and should focus not only on the economic benefits but also on the environmental benefits.
The current study found that OP had significant negative correlation with composting intention. Past research examined the reasons for which residents feel obsessed by composting, e.g., economic benefits or extra income, desire for organic food or fertilizer, a quick way to dispose of biodegradable organic waste and other non-economic factors [99]. To counter this, the government or the local municipality can provide the required facilities, financial assistance, and compost bins to the community at a lower price or free of charge, which in turn reduces the residents’ economic burden of composting. Government assistance and economic support would help the residents perform their composting activities in an efficient manner, without hindering their daily life activities. NGOs or the private sector can be engaged to provide more scientific training or guidance to citizens in order to ensure their composting technique is more productive and less time consuming.

6. Conclusions and Limitations

The determinants in the integrated model used in this study influenced the food waste composting intention among home gardeners. The factors were attitude, subjective norm, harmonious passion, perceived behavioral control and obsessive passion, all of which supported and affected composting intention. Questionnaire surveys were used in high-rise residential areas in Dhaka city. The two-staged analysis demonstrated that harmonious passion was the most significant factor of the current integrated framework; harmonious passion, followed by subjective norm, attitude, perceived behavioral control and obsessive passion. The results indubitably proved that harmonious passion influences food waste composting intention among home gardeners. Examining the behavioral intentions for food waste composting was deemed relevant and considered as an alternative household waste management system that provides a solid foundation for successful food waste management in developing countries like Bangladesh. A multi-analytical approach that integrates the PLS-SEM and ANN was applied in this research to test the proposed research model for developing a new approach to solving the analytical issues in other relevant research fields.
In this present study, there are certain limitations that should be taken into account for future studies. Firstly, the sample of this research only involved residents of Dhaka city. Thus, further research to determine whether the study results can be generalized at the national level should be executed. Next, the sample size of this research is small. The real scenario of food waste composting in various types of locations (high density vs. low density) may possibly be different. More sample data can be gathered from a wider research scope in future studies for analyzing the food waste composting issues countrywide, or nationwide. Additionally, the current study primarily focused on the behavioral intentions of households regarding food waste composting. Even though intention is a good predictor of behavior, it cannot fully represent actual behavior. Hence, future research is important to gather information from residents with home composting. A further improvement would be if the actual behavior of households undertaking food waste composting were further studied, taking into account the aspect of availability of composting bins and the respondents’ preferences and needs. Demographic attributes such as occupation, type of house and gender can potentially be examined as moderators. Finally, the current study examined types of respondents who were involved in rooftop gardening. Future studies could include non-gardeners or other stakeholders to examine food waste composting.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su14159400/s1, Table S1: Application of TPB in waste recycling studies (SLR).

Author Contributions

Conceptualization, S.K.M. and A.R.; Data curation, A.R. and I.M.; Formal analysis, A.R. and I.M.; Investigation, A.R., T.A.P. and G.A.A.; Methodology, A.R.; Project administration, A.R.; Software, A.R.; Supervision, A.R., T.A.P. and G.A.A.; Writing—original draft, A.R. and S.K.M.; Writing—review & editing, A.R., G.A.A. and S.K.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data will be made available upon request from the corresponding author.

Acknowledgments

We are grateful for the support from Ghazanfer Ali Abbasi, Labuan Faculty of International Finance, Universiti Malaysia Sabah.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Martínez-Córdoba, P.J.; Amor-Esteban, V.; Benito, B.; García-Sánchez, I.M. The commitment of spanish local governments to sustainable development goal 11 from a multivariate perspective. Sustainability 2021, 13, 1222. [Google Scholar] [CrossRef]
  2. Lin, Y.; Ma, X.; Peng, X.; Yu, Z. Hydrothermal carbonization of typical components of municipal solid waste for deriving hydrochars and their combustion behavior. Bioresour. Technol. 2007, 243, 539–547. [Google Scholar] [CrossRef] [PubMed]
  3. Wang, S.; Wang, J.; Zhao, S.; Yang, S. Information publicity and resident’s waste separation behavior: An empirical study based on the norm activation model. Waste Manag. 2019, 87, 33–42. [Google Scholar] [CrossRef] [PubMed]
  4. Ding, Y.; Zhao, J.; Liu, J.W.; Zhou, J.; Cheng, L.; Zhao, J.; Hu, Z.T. A review of China’s municipal solid waste (MSW) and comparison with international regions: Management and technologies in treatment and resource utilization. J. Clean. Prod. 2021, 293, 126144. [Google Scholar] [CrossRef]
  5. Alam, O.; Qiao, X. An in-depth review on municipal solid waste management, treatment and disposal in Bangladesh. Sustain. Cities Soc. 2020, 52, 101775. [Google Scholar] [CrossRef]
  6. Sultana, M.M.; Kibria, M.G.; Jahiruddin, M.; Abedin, M.A. Composting constraints and prospects in Bangladesh: A review. J. Geosci. Environ. Prot. 2020, 8, 126. [Google Scholar] [CrossRef]
  7. Rahman, M.A.; Saha, C.K.; Feng, L.; Møller, H.B.; Alam, M.M. Anaerobic digestion of agro-industrial wastes of Bangladesh: Influence of total solids content. Eng. Agric. Environ. Food 2019, 12, 484–493. [Google Scholar] [CrossRef]
  8. Kabir, M.H.; Rainis, R. Adoption and intensity of integrated pest management (IPM) vegetable farming in Bangladesh: An approach to sustainable agricultural development. Environ. Dev. Sustain. 2015, 17, 1413–1429. [Google Scholar] [CrossRef]
  9. Tsai, W.C.; Chen, X.; Yang, C. Consumer food waste behavior among emerging adults: Evidence from China. Foods 2020, 9, 961. [Google Scholar] [CrossRef]
  10. Thi, N.B.D.; Kumar, G.; Lin, C.Y. An overview of food waste management in developing countries: Current status and future perspective. J. Environ. Manag. 2015, 157, 220–229. [Google Scholar]
  11. Shams, S.; Sahu, J.N.; Rahman, S.S.; Ahsan, A. Sustainable waste management policy in Bangladesh for reduction of greenhouse gases. Sustain. Cities Soc. 2017, 33, 18–26. [Google Scholar] [CrossRef]
  12. Chowdhary, P.; Bharagava, R.N.; Mishra, S.; Khan, N. Role of industries in water scarcity and its adverse effects on environment and human health. Environ. Concerns Sustain. Dev. 2020, 28, 235–256. [Google Scholar] [CrossRef]
  13. United Nations. Department of Economic and Social Affairs. Population Division. The World’s Cities in 2018—Data Booklet (ST/ESA/SER.A/417). 2018. Available online: https://www.un.org/en/events/citiesday/asets/pdf/the_worlds_cities_in_2018_data_booklet.pdf (accessed on 29 April 2022).
  14. Yang, F.; Li, Y.; Han, Y.; Qian, W.; Li, G.; Luo, W. Performance of mature compost to control gaseous emissions in kitchen waste composting. Sci. Total Environ. 2019, 657, 262–269. [Google Scholar] [CrossRef] [PubMed]
  15. Yang, F.; Li, G.; Shi, H.; Wang, Y. Effects of phosphogypsum and superphosphate on compost maturity and gaseous emissions during kitchen waste composting. Waste Manag. 2015, 36, 70–76. [Google Scholar] [CrossRef]
  16. Lin, L.; Xu, F.; Ge, X.; Li, Y. Improving the sustainability of organic waste management practices in the food-energy-water nexus: A comparative review of anaerobic digestion and composting. Renew. Sustain. Energy Rev. 2018, 89, 151–167. [Google Scholar] [CrossRef]
  17. Cheng, H.; Li, Y.; Li, L.; Chen, R.; Li, Y.Y. Long-term operation performance and fouling behavior of a high-solid anaerobic membrane bioreactor in treating food waste. Chem. Eng. J. 2020, 394, 124918. [Google Scholar] [CrossRef]
  18. Ajmal, M.; Shi, A.; Awais, M.; Mengqi, Z.; Zihao, X.; Shabbir, A.; Ye, L. Ultra-high temperature aerobic fermentation pretreatment composting: Parameters optimization, mechanisms and compost quality assessment. J. Environ. Chem. Eng. 2021, 9, 105453. [Google Scholar] [CrossRef]
  19. Lu, H.R.; Qu, X.; El Hanandeh, A. Towards a better environment-the municipal organic waste management in Brisbane: Environmental life cycle and cost perspective. J. Clean. Prod. 2020, 258, 120756. [Google Scholar] [CrossRef]
  20. Andersen, J.K.; Boldrin, A.; Christensen, T.H.; Scheutz, C. Home composting as an alternative treatment option for organic household waste in Denmark: An environmental assessment using life cycle assessment-modelling. Waste Manag. 2020, 32, 31–40. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Andersen, J.K.; Boldrin, A.; Christensen, T.H.; Scheutz, C. Mass balances and life cycle inventory of home composting of organic waste. Waste Manag. 2011, 31, 1934–1942. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Barrena, R.; Font, X.; Gabarrell, X.; Sánchez, A. Home composting versus industrial composting: Influence of composting system on compost quality with focus on compost stability. Waste Manag. 2014, 34, 1109–1116. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Colón, J.; Martínez-Blanco, J.; Gabarrell, X.; Artola, A.; Sánchez, A.; Rieradevall, J.; Font, X. Environmental assessment of home composting. Resour. Conserv. Recycl. 2010, 54, 893–904. [Google Scholar] [CrossRef] [Green Version]
  24. Jara-Samaniego, J.; Pérez-Murcia, M.D.; Bustamante, M.A.; Pérez-Espinosa, A.; Paredes, C.; López, M.; Moral, R. Composting as sustainable strategy for municipal solid waste management in the Chimborazo Region, Ecuador: Suitability of the obtained composts for seedling production. J. Clean. Prod. 2017, 141, 1349–1358. [Google Scholar] [CrossRef]
  25. Saer, A.; Lansing, S.; Davitt, N.H.; Graves, R.E. Life cycle assessment of a food waste composting system: Environmental impact hotspots. J. Clean. Prod. 2013, 52, 234–244. [Google Scholar] [CrossRef]
  26. Fernando, R.L.S. People's participation in home composting: An exploratory study based on Moratuwa and Kaduwela municipalities in the Western Province of Sri Lanka. Manag. Environ. Qual. 2021, 32, 344–358. [Google Scholar] [CrossRef]
  27. Faverial, J.; Sierra, J. Home composting of household biodegradable wastes under the tropical conditions of Guadeloupe (French Antilles). J. Clean. Prod. 2014, 83, 238–244. [Google Scholar] [CrossRef]
  28. Getahun, T.; Nigusie, A.; Entele, T.; Van Gerven, T.; Van der Bruggen, B. Effect of turning frequencies on composting biodegradable municipal solid waste quality. Resour Conserv Recycl. 2012, 65, 79–84. [Google Scholar] [CrossRef]
  29. Srivastava, V.; Vaish, B.; Singh, R.P.; Singh, P. An insight to municipal solid waste management of Varanasi city, India, and appraisal of vermicomposting as its efficient management approach. Environ. Monit. Assess. 2020, 192, 191. [Google Scholar] [CrossRef] [PubMed]
  30. Ulhasanah, N.; Goto, N. Assessment of citizens’ environmental behavior toward municipal solid waste management for a better and appropriate system in Indonesia: A case study of Padang City. J. Mater. Cycles Waste Manag. 2018, 20, 1257–1272. [Google Scholar] [CrossRef]
  31. Matter, A.; Ahsan, M.; Marbach, M.; Zurbrügg, C. Impacts of policy and market incentives for solid waste recycling in Dhaka, Bangladesh. Waste Manag. 2015, 39, 321–328. [Google Scholar] [CrossRef]
  32. Loan, L.T.T.; Takahashi, Y.; Nomura, H.; Yabe, M. Modeling home composting behavior toward sustainable municipal organic waste management at the source in developing countries. Resour. Conserv. Recycl. 2019, 140, 65–71. [Google Scholar] [CrossRef]
  33. Al Mamun, A.; Hayat, N.; Malarvizhi, C.A.N.; Zainol, N.R.B. Economic and environmental sustainability through green composting: A study among low-income households. Sustainability 2020, 12, 6488. [Google Scholar] [CrossRef]
  34. Dwinadine, Y.; Dewi, E. The Purpose of Intention to Use Composting For Waste Management in Bandung Area. Asian J. Res. Bus. Manag. 2020, 2, 197–206. [Google Scholar]
  35. Najmi, A.; Kanapathy, K.; Aziz, A.A. Understanding consumer participation in managing ICT waste: Findings from two-staged Structural Equation Modeling–Artificial Neural Network approach. Environ. Sci. Pollut. Res. 2020, 28, 14782–14796. [Google Scholar] [CrossRef] [PubMed]
  36. Alam, M.M.D.; Alam, M.Z.; Rahman, S.A.; Taghizadeh, S.K. Factors influencing mHealth adoption and its impact on mental well-being during COVID-19 pandemic: A SEM-ANN approach. J. Biomed. Inform. 2021, 116, 103722. [Google Scholar] [CrossRef]
  37. Setiawan, B.; Afiff, A.Z.; Heruwasto, I. Integrating the theory of planned behavior with norm activation in a pro-environmental context. Soc. Mark. Q. 2020, 26, 244–258. [Google Scholar] [CrossRef]
  38. Wu, Z.; Ann, T.W.; Shen, L. Investigating the determinants of contractor’s construction and demolition waste management behavior in Mainland China. Waste Manag. 2017, 60, 290–300. [Google Scholar] [CrossRef]
  39. Ajzen, I. The theory of planned behaviour is alive and well, and not ready to retire: A commentary on Sniehotta, Presseau, and Araújo-Soares. Health Psychol. Rev. 2015, 9, 131–137. [Google Scholar] [CrossRef] [PubMed]
  40. De Groot, J.I.; Steg, L. Morality and prosocial behavior: The role of awareness, responsibility, and norms in the norm activation model. J. Soc. Psychol. 2009, 149, 425–449. [Google Scholar] [CrossRef]
  41. Ajzen, I.; Fishbein, M. Attitudes and the attitude-behavior relation: Reasoned and automatic processes. Eur. Rev. Soc. Psychol. 2000, 11, 1–33. [Google Scholar] [CrossRef]
  42. Wang, Z.; Guo, D.; Wang, X. Determinants of residents’e-waste recycling behaviour intentions: Evidence from China. J. Clean. Prod. 2016, 137, 850–860. [Google Scholar] [CrossRef]
  43. Wang, Z.; Guo, D.; Wang, X.; Zhang, B.; Wang, B. How does information publicity influence residents’ behaviour intentions around e-waste recycling? Resources. Conserv. Recycl. 2018, 133, 1–9. [Google Scholar] [CrossRef]
  44. Mak, T.M.W.; Yu, I.K.M.; Wang, L. Extended theory of planned behaviour for promoting construction waste recycling in Hong Kong. Waste Manag. 2019, 83, 161–170. [Google Scholar] [CrossRef] [PubMed]
  45. Abbasi, G.A.; Kumaravelu, J.; Goh, Y.N.; Singh, K.S.D. Understanding the intention to revisit a destination by expanding the theory of planned behaviour (TPB). Span. J. Mark. -ESIC 2021, 25, 280–305. [Google Scholar] [CrossRef]
  46. Hu, H.; Zhang, J.; Wang, C.; Yu, P.; Chu, G. What influences tourists’ intention to participate in the Zero Litter Initiative in mountainous tourism areas: A case study of Huangshan National Park, China. Sci. Total Environ. 2019, 657, 1127–1137. [Google Scholar] [CrossRef]
  47. Wang, S.; Wang, J.; Yang, S.; Li, J.; Zhou, K. From intention to behavior: Comprehending residents’ waste sorting intention and behavior formation process. Waste Manag. 2020, 113, 41–50. [Google Scholar]
  48. Li, J.; Zuo, J.; Cai, H.; Zillante, G. Construction waste reduction behavior of contractor employees: An extended theory of planned behavior model approach. J. Clean. Prod. 2018, 172, 1399–1408. [Google Scholar] [CrossRef]
  49. Ajzen, I. The theory of planned behavior. Organ. Behav Hum. Decis Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  50. Vallerand, R.J. On passion for life activities: The dualistic model of passion. In Advances in Experimental Social Psychology; Academic Press: Cambridge, MA, USA, 2010; pp. 97–193. [Google Scholar]
  51. Vallerand, R.J.; Blanchard, C.; Mageau, G.A.; Koestner, R.; Ratelle, C.; Leonard, M.; Marsolais, J. Les passions de l'Ame: On obsessive and harmonious passion. J. Personal. Soc. Psychol. 2003, 85, 756–767. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Vallerand, R.J.; Houlfort, N. Passion at work: Toward a new conceptualization. Soc. Issues Manag. 2003, 7, 175–204. [Google Scholar]
  53. Deci, E.L.; Ryan, R.M. The” what” and” why” of goal pursuits: Human needs and the self-determination of behavior. Psychol. Inq. 2000, 11, 227–268. [Google Scholar] [CrossRef]
  54. Deci, E.L.; Eghrari, H.; Patrick, B.C.; Leone, D.R. Facilitating internalization: The self-determination theory perspective. J. Personal. 1994, 62, 119–142. [Google Scholar] [CrossRef] [PubMed]
  55. Vallerand, R.J.; Fortier, M.S.; Guay, F. Self-determination and persistence in a real-life setting: Toward a motivational model of high school dropout. J. Personal. Soc. Psychol. 1997, 72, 1117–1161. [Google Scholar] [CrossRef]
  56. Mageau, G.A.; Carpentier, J.; Vallerand, R.J. The role of self-esteem contingencies in the distinction between obsessive and harmonious passion. Eur. J. Soc. Psychol. 2011, 41, 720–729. [Google Scholar] [CrossRef]
  57. Seguin-Levesque, C.; Lalibertea, M.L.N.; Pelletier, L.G.; Blanchard, C.; Vallerand, R.J. Harmonious and obsessive passion for the Internet: Their Associations with the Couple's relationship 1. J. Appl. Soc. Psychol. 2003, 33, 197–221. [Google Scholar] [CrossRef]
  58. Verner-Filion, J.; Vallerand, R.J. On the differential relationships involving perfectionism and academic adjustment: The mediating role of passion and affect. Learn. Individ. Differ. 2016, 50, 103–113. [Google Scholar] [CrossRef]
  59. Vallerand, R.J. The Psychology of Passion: A Dualistic Model; Oxford University Press: Oxford, UK, 2015. [Google Scholar]
  60. Hodgins, H.S.; Knee, C.R. The integrating self and conscious experience. Handb. Self-Determ. Res. 2002, 87, 86–98. [Google Scholar]
  61. Afsar, B.; Badir, Y.; Kiani, U.S. Linking spiritual leadership and employee pro-environmental behavior: The influence of workplace spirituality, intrinsic motivation, and environmental passion. J. Environ. Psychol. 2016, 45, 79–88. [Google Scholar] [CrossRef]
  62. Li, Z.; Xue, J.; Li, R.; Chen, H.; Wang, T. Environmentally specific transformational leadership and employee’s pro-environmental behavior: The mediating roles of environmental passion and autonomous motivation. Front. Psychol. 2020, 11, 1408. [Google Scholar] [CrossRef]
  63. Akhshik, A.; Ozturen, A.; Rezapouraghdam, H. A passionate travel to mind green turtles—Unpacking the complexity of visitors' green behaviour. Int. J. Tour. Res. 2021, 23, 301–318. [Google Scholar] [CrossRef]
  64. Islam, M.; Al Nayeem, A.; Majumder, A.K.; Elahi, K.T. Study on the Status of Roof Top Gardening in Selected Residential Areas of Dhaka City, Bangladesh. Malays. J. Sustain. Agric. 2019, 3, 31–34. [Google Scholar] [CrossRef]
  65. Ahsan, T.; Zaman, A.U. Household waste management in high-rise residential building in Dhaka, Bangladesh: Users’ perspective. Int. J. Waste Resour. 2014, 4, 1000133. [Google Scholar]
  66. Onel, N.; Mukherjee, A. Why do consumers recycle? A holistic perspective encompassing moral considerations, affective responses, and self-interest motives. Psychol. Mark. 2017, 34, 956–971. [Google Scholar] [CrossRef]
  67. Lam, T.W.L.; Tsui, Y.C.J.; Fok, L.; Cheung, L.T.O.; Tsang, E.P.K.; Lee, J.C.K. The influences of emotional factors on householders’ decarbonizing cooling behaviour in a subtropical Metropolitan City: An application of the extended theory of planned behaviour. Sci. Total Environ. 2022, 807, 150826. [Google Scholar] [CrossRef] [PubMed]
  68. Zhang, D.; Huang, G.; Yin, X.; Gong, Q. Residents’ waste separation behaviors at the source: Using SEM with the theory of planned behavior in Guangzhou, China. Int. J. Environ. Res. Public Health 2015, 12, 9475–9491. [Google Scholar] [CrossRef]
  69. Stoeva, K.; Alriksson, S. Influence of recycling programmes on waste separation behaviour. Waste Manag. 2017, 68, 732–741. [Google Scholar] [CrossRef]
  70. Fornara, F.; Carrus, G.; Passafaro, P.; Bonnes, M. Distinguishing the sources of normative influence on proenvironmental behaviors: The role of local norms in household waste recycling. Group Processes Intergroup Relat. 2011, 14, 623–635. [Google Scholar] [CrossRef]
  71. Wang, Y.C.; Huang, Y.C.; Yang, M. Effects of green brand on green purchase intention. Mark. Intell. Plan. 2014, 32, 250–268. [Google Scholar] [CrossRef]
  72. Ayob, S.F.; Sheau-Ting, L.; Abdul Jalil, R.; Chin, H.-C. Key determinants of waste separation intention: Empirical application of TPB. Facilities 2017, 35, 696–708. [Google Scholar] [CrossRef] [Green Version]
  73. Yuan, Y.; Nomura, H.; Takahashi, Y.; Yabe, M. Model of Chinese household kitchen waste separation behavior: A case study in Beijing city. Sustainability 2016, 8, 1083. [Google Scholar] [CrossRef] [Green Version]
  74. Ramayah, T.; Lee, J.W.C.; Lim, S. Sustaining the environment through recycling: An empirical study. J. Environ. Manag. 2012, 102, 141–147. [Google Scholar] [CrossRef] [PubMed]
  75. Ertz, M.; Huang, R.; Jo, M.S.; Karakas, F.; Sarigöllü, E. From single-use to multi-use: Study of consumers’ behavior toward consumption of reusable containers. J. Environ. Manag. 2017, 193, 334–344. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Zhang, L.; Hu, Q.; Zhang, S.; Zhang, W. Understanding Chinese Residents’ Waste Classification from a Perspective of Intention–Behavior Gap. Sustainability 2020, 12, 4135. [Google Scholar] [CrossRef]
  77. Xu, L.; Ling, M.; Lu, Y.; Shen, M. Understanding household waste separation behaviour: Testing the roles of moral, past experience, and perceived policy effectiveness within the theory of planned behaviour. Sustainability 2017, 9, 625. [Google Scholar] [CrossRef] [Green Version]
  78. Kidwell, B.; Jewell, R.D. An examination of perceived behavioral control: Internal and external influences on intention. Psychol. Mark. 2003, 20, 625–642. [Google Scholar] [CrossRef]
  79. Wang, Q.; Tian, D.; Hu, J.; Huang, M.; Shen, F.; Zeng, Y.; He, J. Harvesting bacterial cellulose from kitchen waste to prepare superhydrophobic aerogel for recovering waste cooking oil toward a closed-loop biorefinery. ACS Sustain. Chem. Eng. 2020, 8, 13400–13407. [Google Scholar] [CrossRef]
  80. Junot, A.; Paquet, Y.; Martin-Krumm, C. Passion for outdoor activities and environmental behaviors: A look at emotions related to passionate activities. J. Environ. Psychol. 2017, 53, 177–184. [Google Scholar] [CrossRef]
  81. Webb, D.; Soutar, G.N.; Mazzarol, T.; Saldaris, P. Self-determination theory and consumer behavioural change: Evidence from a household energy-saving behaviour study. J. Environ. Psychol. 2013, 35, 59–66. [Google Scholar] [CrossRef]
  82. Waugh, C.E.; Fredrickson, B.L. Nice to know you: Positive emotions, self–other overlap, and complex understanding in the formation of a new relationship. J. Posit. Psychol. 2006, 1, 93–106. [Google Scholar] [CrossRef]
  83. Gousse-Lessard, A.S.; Vallerand, R.J.; Carbonneau, N.; Lafrenière, M.A.K. The role of passion in mainstream and radical behaviors: A look at environmental activism. J. Environ. Psychol. 2013, 35, 18–29. [Google Scholar] [CrossRef]
  84. Fredrickson, B.L. The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. Am. Psychol. 2001, 56, 218. [Google Scholar] [CrossRef] [PubMed]
  85. Rastegari Kopaei, H.; Nooripoor, M.; Karami, A.; Petrescu-Mag, R.M.; Petrescu, D.C. Drivers of residents’ home composting intention: Integrating the theory of planned behavior, the norm activation model, and the moderating role of composting knowledge. Sustainability 2021, 12, 6826. [Google Scholar] [CrossRef]
  86. Manabika, S.; Ahmed, M.B.; Khan, S.A.K.U.; Islam, M.M. Present scenario and problem confrontation of rooftop gardening and its efficacy in ambient environment reclamation in Khulna City of Bangladesh. Fundam. Appl. Agric. 2019, 4, 617–626. [Google Scholar]
  87. Adib, A.; Mahapatro, M. Private sector involvement in waste management of metropolises: Insights from Dhaka city. Waste Manag. 2022, 142, 143–151. [Google Scholar] [CrossRef] [PubMed]
  88. Bird, J.; Li, Y.; Rahman, H.Z.; Rama, M.; Venables, A.J. Toward Great Dhaka: A New Urban Development Paradigm Eastward; World Bank: Washington, DC, USA, 2018. [Google Scholar]
  89. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879. [Google Scholar] [CrossRef]
  90. Harman, H.H. Modern Factor Analysis; University of Chicago Press: Chicago, IL, USA, 1976. [Google Scholar]
  91. Abbasi, G.A.; Abdul Rahim, N.F.; Wu, H.; Iranmanesh, M.; Keong, B.N.C. Determinants of SME’s Social Media Marketing Adoption: Competitive Industry as a Moderator. SAGE Open 2022, 12, 21582440211067220. [Google Scholar] [CrossRef]
  92. Ashaari, M.A.; Singh, K.S.D.; Abbasi, G.A.; Amran, A.; Liebana-Cabanillas, F.J. Big data analytics capability for improved performance of higher education institutions in the Era of IR 4.0: A multi-analytical SEM & ANN perspective. Technol. Forecast. Soc. Chang. 2021, 173, 121119. [Google Scholar]
  93. Alzahrani, L.; Al-Karaghouli, W.; Weerakkody, V. Analysing the critical factors influencing trust in e-government adoption from citizens’ perspective: A systematic review and a conceptual framework. Int. Bus. Rev. 2017, 26, 164–175. [Google Scholar] [CrossRef]
  94. Hair, J.F., Jr.; Sarstedt, M.; Hopkins, L.; Kuppelwieser, V.G. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. Eur. Bus. Rev. 2014, 26, 106–121. [Google Scholar] [CrossRef]
  95. Mahmud, I.; Sultana, S.; Rahman, A.; Ramayah, T.; Cheng Ling, T. E-waste recycling intention paradigm of small and medium electronics store managers in Bangladesh: An S–O–R perspective. Waste Manag. Res. 2020, 38, 1438–1449. [Google Scholar] [CrossRef]
  96. Nasrin, S.; Islam, M.M.; Mannan, M.A.; Ahmed, M.B. Women participation in rooftop gardening in some areas of Khulna city. Bangladesh J. Agric. Res. 2019, 44, 327–337. [Google Scholar] [CrossRef]
  97. Mahmud, I.; Ramayah, T.; Kurnia, S. To use or not to use: Modelling end user grumbling as user resistance in pre-implementation stage of enterprise resource planning system. Inf. Syst. 2017, 69, 164–179. [Google Scholar] [CrossRef]
  98. Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
  99. Fornell, C.; Larcker, D.F. Structural equation models with unobservable variables and measurement error. Algebra Stat. 1981, 18, 382–388. [Google Scholar]
  100. Hair, J.F., Jr.; Sarstedt, M.; Matthews, L.M.; Ringle, C.M. Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part I–method. Eur. Bus. Rev. 2016, 28, 63–76. [Google Scholar] [CrossRef]
  101. Chin, W.W. How to write up and report PLS analyses. In Handbook of Partial Least Squares; Springer: Berlin/Heidelberg, Germany, 2010; pp. 655–690. [Google Scholar]
  102. Cohen, S. Perceived Stress in a Probability Sample of the United States; Sage Publications: Thousand Oaks, CA, USA, 1988; pp. 31–67. [Google Scholar]
  103. Hair, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M.; Thiele, K.O. Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. J. Acad. Mark. Sci. 2017, 45, 616–632. [Google Scholar] [CrossRef]
  104. Al-Emran, M.; Abbasi, G.A.; Mezhuyev, V. Evaluating the impact of knowledge management factors on M-learning adoption: A deep learning-based hybrid SEM-ANN approach. In Recent Advances in Technology Acceptance Models and Theories; Springer: Cham, Switzerland, 2021; pp. 159–172. [Google Scholar]
  105. Leong, L.Y.; Hew, T.S.; Ooi, K.B.; Lee, V.H.; Hew, J.J. A hybrid SEM-neural network analysis of social media addiction. Expert Syst. Appl. 2019, 133, 296–316. [Google Scholar] [CrossRef]
  106. Chong, A.Y.L. A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Syst. Appl. 2013, 40, 1240–1247. [Google Scholar] [CrossRef]
  107. Halder, P.; Singh, H. Predictors of recycling intentions among the youth: A developing country perspective. Recycling 2018, 3, 38. [Google Scholar] [CrossRef] [Green Version]
  108. Ajzen, I.; Fishbein, M. Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychol. Bull. 1977, 84, 888. [Google Scholar] [CrossRef]
  109. Afroz, R.; Muhibbullah, M.; Farhana, P.; Morshed, M.N. Analysing the intention of the households to drop off mobile phones to the collection boxes: Empirical study in Malaysia. Ecofeminism Clim. Chang. 2020, 1, 3–20. [Google Scholar] [CrossRef]
  110. Oztekin, C.; Teksöz, G.; Pamuk, S.; Sahin, E.; Kilic, D.S. Gender perspective on the factors predicting recycling behavior: Implications from the theory of planned behavior. Waste Manag. 2017, 62, 290–302. [Google Scholar] [CrossRef] [PubMed]
  111. Kumar, A. Exploring young adults’e-waste recycling behaviour using an extended theory of planned behaviour model: A cross-cultural study. Resour. Conserv. Recycl. 2019, 141, 378–389. [Google Scholar] [CrossRef]
  112. Khan, F.; Ahmed, W.; Najmi, A. Understanding consumers’ behavior intentions towards dealing with the plastic waste: Perspective of a developing country. Resour. Conserv. Recycl. 2019, 142, 49–58. [Google Scholar] [CrossRef]
  113. Wu, L.; Li, Z.; Zhao, F.; Zhao, B.; Phillip, F.O.; Feng, J.; Yu, K. Increased organic fertilizer and reduced chemical fertilizer increased fungal diversity and the abundance of beneficial fungi on the grape berry surface in arid areas. Front. Microbiol. 2021, 12, 1066. [Google Scholar] [CrossRef] [PubMed]
  114. Allameh, S.M.; Khazaei Pool, J.; Jaberi, A.; Salehzadeh, R.; Asadi, H. Factors influencing sport tourists’ revisit intentions: The role and effect of destination image, perceived quality, perceived value and satisfaction. Asia Pac. J. Mark. Logist. 2015, 27, 191–207. [Google Scholar] [CrossRef]
  115. Chang, M.L.; Taxer, J. Teacher emotion regulation strategies in response to classroom mis behavior. Teach. Teach. 2021, 27, 353–369. [Google Scholar] [CrossRef]
  116. Sharma, A.; Dwivedi, Y.K.; Arya, V.; Siddiqui, M.Q. Does SMS advertising still have relevance to increase consumer purchase intention? A hybrid PLS-SEM-neural network modelling approach. Comput. Hum. Behav. 2021, 124, 1–16. [Google Scholar] [CrossRef]
Figure 1. Conceptual framework.
Figure 1. Conceptual framework.
Sustainability 14 09400 g001
Figure 2. PLS results.
Figure 2. PLS results.
Sustainability 14 09400 g002
Figure 3. ANN model for the intention to compost food waste. (ATT, Attitude; HP, Harmonious passion; OP, Obsessive passion; PBC, Perceived behavioral control; SN, Subjective norm.)
Figure 3. ANN model for the intention to compost food waste. (ATT, Attitude; HP, Harmonious passion; OP, Obsessive passion; PBC, Perceived behavioral control; SN, Subjective norm.)
Sustainability 14 09400 g003
Table 1. List of constructs and measurement items.
Table 1. List of constructs and measurement items.
Research
Variable
CodeQuestionsReference
Scale
AttitudeATT1I think composting is rewarding.[85]
ATT2I think composting is hygienic.
ATT3I think composting is a good idea.
ATT4I think composting is necessary.
Subjective normSN1Many of my friends find food waste composting is useful.[85]
SN2Many of my relatives/neighbours find food waste composting is useful.
SN3People who influence my decisions think that I should compost food waste.
Perceived behavioral controlPBC1I know what items can be composted among my food waste.[85]
PBC2I have tools to compost my food waste.
PBC3I know how to compost my food waste.
PBC4I have enough time for composting.
Harmonious PassionHP1My composting practices are in harmony with the other activities in my life.[63]
HP2The new things that I discover with composting activity allow me to appreciate it even more.
HP3My composting activities are well integrated in my life.
Obsessive PassionOP1I have difficulties controlling my urge to do composting at home.[63]
OP2My composting activities are so exciting that I sometimes lose control over them.
OP3If I could, I would only do composting activities.
Composting IntentionINT1I have the intention to buy the waste composting equipment.[85]
INT2I have the intention to compost kitchen waste/food waste.
INT3I have level of planning to buy the composting equipment.
INT4I have level of planning to compost kitchen waste/food waste.
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
VariablenPercentage
Gender
Male3316.2%
Female17083.7%
Employment status
Employed5426.6%
Housewives/Non-employed14973.3%
Income level (Tk/month/family)
Tk 10,000–30,000167.8%
Tk 30,001–50,0007536.9%
Tk 50,001–70,0002311.33%
Tk 70,001–90,0007743.8%
>TK90,000125.9%
Age
18–30115.4%
31–404220.6%
41–5011553.2%
>513517.2%
Table 3. Results of the confirmatory factor analysis.
Table 3. Results of the confirmatory factor analysis.
ConstructsStandardized
Factor Loading
Cronbach’s AlphaComposite Reliabilityrho_AAVE
AttitudeATT 1 0.763
ATT 2 0.827
ATT 3 0.839
ATT 4 0.794
0.8210.8810.8270.650
Harmonious passionHP 1 0.867
HP 2 0.877
HP 3 0.864
0.8390.9030.8390.756
IntentionINT 1 0.743
INT 2 0.824
INT 3 0.767
INT 4 0.672
0.7440.8390.7510.568
Obsessive passionOP 1 0.923
OP 2 0.911
OP 3 0.892
0.8990.9340.9980.825
Perceived behavioral controlPBC 1 0.827
PBC 2 0.825
PBC 3 0.830
PBC 4 0.820
0.8450.8960.8470.682
Subjective normSN 1 0.901
SN 2 0.926
SN 3 0.907
0.8980.9370.9050.831
AVE: average variance extracted.
Table 4. Discriminant validity.
Table 4. Discriminant validity.
ATTHPINTOPPBCSN
ATT0.806
HP0.4220.869
INT0.5880.5990.753
OP0.0100.076−0.0550.908
PBC0.3160.2650.5060.0480.826
SN0.3500.3720.5600.1670.3370.912
ATT: Attitude; HP: Harmonious passion. SN: Subjective norms; OP: Obsessive passion PBC: Perceived behavior control; INT: Intention.
Table 5. Predictor power of construct ( R 2 & Q 2 ).
Table 5. Predictor power of construct ( R 2 & Q 2 ).
ConstructR2Q2
INT0.6470.33
Table 6. Strength of effect.
Table 6. Strength of effect.
RelationshipEffect SizeRemark
ATT→ INT0.163Medium
SN→ INT0.174Medium
PBC→ INT0.141Small
HP→INT0.223Medium
OP→ INT0.055Small
Note: ATT: Attitude; HP: Harmonious passion. SN: Subjective norms; OP: Obsessive passion PBC: Perceived behavioral control; INT: Intention.
Table 7. Hypothesis test result.
Table 7. Hypothesis test result.
HypothesisRelationshipPath Co-EfficientT Statisticsp-ValueRemark
H1ATT → INT0.277 ***5.2980.000Supported
H2SN → INT0.285 ***4.7520.000Supported
H3PBC → INT0.245 ***4.2980.000Supported
H4HP → INT0.322 ***6.3590.000Supported
H5OP → INT−0.142 *2.3810.018Supported
ATT: Attitude; HP: Harmonious passion. SN: Subjective norms; OP: Obsessive passion PBC: Perceived behavior control; INT: Intention. * p < 0.05, *** p < 0.001.
Table 8. Root Mean Square Error (RMSE) for ANN model.
Table 8. Root Mean Square Error (RMSE) for ANN model.
NetworkSampleTrainingSampleTestingTotal Sample
ANN11810.350220.312203
ANN21870.456160.343203
ANN31810.416220.302203
ANN41790.388240.372203
ANN51820.469210.461203
ANN61830.362200.376203
ANN71810.383220.380203
ANN81850.338180.453203
ANN91780.353250.327203
ANN101820.401210.228203
Mean-0.392Mean0.355-
St. dev-0.045St. dev0.070-
Table 9. Neural network sensitivity analysis.
Table 9. Neural network sensitivity analysis.
NetworkATTHPOPPBCSN
10.641.000.290.890.88
20.650.580.091.000.19
30.721.000.330.980.82
41.000.970.100.440.55
50.470.710.281.000.25
60.681.000.290.670.72
70.651.000.100.560.59
80.801.000.250.680.49
91.000.790.220.780.55
100.591.000.260.720.75
Average importance0.720.900.220.770.58
Normalized importance (%)80%100%24%85%64%
Ranking31524
ATT: Attitude; HP: Harmonious passion. SN: Subjective norms; OP: Obsessive passion PBC: Perceived behavior control; INT: Intention.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Rahman, A.; Ai Ping, T.; Mubeen, S.K.; Mahmud, I.; Abbasi, G.A. What Influences Home Gardeners’ Food Waste Composting Intention in High-Rise Buildings in Dhaka Megacity, Bangladesh? An Integrated Model of TPB and DMP. Sustainability 2022, 14, 9400. https://doi.org/10.3390/su14159400

AMA Style

Rahman A, Ai Ping T, Mubeen SK, Mahmud I, Abbasi GA. What Influences Home Gardeners’ Food Waste Composting Intention in High-Rise Buildings in Dhaka Megacity, Bangladesh? An Integrated Model of TPB and DMP. Sustainability. 2022; 14(15):9400. https://doi.org/10.3390/su14159400

Chicago/Turabian Style

Rahman, Ashikur, Teoh Ai Ping, Syeda Khadija Mubeen, Imran Mahmud, and Ghazanfer Ali Abbasi. 2022. "What Influences Home Gardeners’ Food Waste Composting Intention in High-Rise Buildings in Dhaka Megacity, Bangladesh? An Integrated Model of TPB and DMP" Sustainability 14, no. 15: 9400. https://doi.org/10.3390/su14159400

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop