Sage Journals: Discover world-class research

Abstract

Concerns about income inequality and environmental pollution have been important aspects of achieving sustainable development goals. Promotion and use of organic food is one important way to reduce resource consumption and waste. In this study, the structural equation model (SEM) was used to analyze 450 questionnaire data, and the environmental values and perceived consumer effectiveness were used as mediating variables to explore the influence mechanism of income quality on the organic food purchase intention. An extended Value—Attitude—Behavior (VAB) theory was used as the research framework, and the results showed that: (1) income quality had a significant positive effect on environmental values, perceived consumer effectiveness, and organic food purchase intention; (2) perceived consumer effectiveness had a significant positive effect on organic food purchase intention and played a partial mediating role; and (3) environmental values and perceived consumer effectiveness played a chain mediating role between income quality and organic food purchase intention.

Plain Language Summary

Organic food pays attention to the coordinated development with the natural environment, which has the characteristics of high quality, pure green and pollution-free. Compared with other foods (such as green foods, conventioanl foods), organic food have the highest sustainable attributes. This study aims to promote organic food consumption in China. Previous scholars have focused on the quantitative aspect of income, but this study introduces income quality into the study of organic food consumption and proves that income quality has good applicability in the study of organic food consumption. This study theoretically improves the explanation and prediction of organic food consumption, and in practice helps governments, certification agencies, agricultural enterprises and other entities to formulate reasonable and effective organic food management and strategic marketing from the perspective of consumers.

Keywords

income quality organic food purchase intention environmental values perceived

Introduction

Organic food has gained the trust of Chinese consumers (Yang et al., 2021; Zheng, Wen, et al., 2022). The organic label serves as a signal to convey information about the quality and safety of products to consumers. It is considered safer and healthier (Li et al., 2020; Nagaraj, 2021). Thus, demand for organic food from Chinese consumers has increased in recent years (Willer & Lernoud, 2020). Even though China has the potential to become an organic market leader, the actual market share is still small (Liu et al., 2021). According to the global organic agriculture statistical yearbook 2020, China only accounts for 8.3% of the global organic market (Willer & Lernoud, 2020). How to expand the organic market in China is a hot issue.

In studies, the main constraint for consumers is income (see Aschemann-Witzel & Zielke, 2017). Organic food is grown with high inputs, and costs more (Y. Wang et al., 2017), resulting in higher prices. In terms of the quantity, higher levels of consumer income will promote the emergence of a desire for higher-quality food (B. Wang, 2016). Darmon and Drewnowski (2015) showed that when income levels exceed the middle-income line, people’s consumption preferences changed to self-development and health-friendly consumption options. There is strong evidence that higher income groups are health conscious and willing to spend money on foods without additives or preservatives, which are more expensive but of higher quality.

China’s economic development has entered a new era and Chinese residents’ income continues to rise in recent years (Kakwani et al., 2022). Despite the steady rise, many studies show that income quality is an important aspect of income growth. Income can be divided into two dimensions, one is the quantity of income, and the other is the quality of income (Kong & Wang, 2013). J. Wang and Xu (2010) were the first to propose the concept of income quality, suggesting that differences in income quality trigger different behavioral characteristics of consumers with the same income quantity. Luo and Chen (2020) explored the role of income quality as a mediator for promoting rural household consumption. Deng et al. (2020) explored the positive effect of income quality as a core variable with farmers’ entrepreneurial intentions. An et al. (2022) found that income quality significantly and positively influenced farmers’ straw return technology adoption behavior. Focusing on the field of agricultural consumption, only Liu et al. (2019) tested the enhancing effect of income quality on branded fresh meat as the study object. Among the four dimensions, no significant effect of income structure on consumption level was found. Since utilitarianism has focused on the quantitative attributes of income, the analysis of its qualitative attributes has remained limited (Liu et al., 2019). This is evidenced by the lack of studies that examine the relationship between organic food purchase intentions and income. Income variables are generally included as control variables in models (Janssen, 2018; Tandon et al., 2020). However, even for the regression coefficients of the income variable that appear as control variables only, the existing studies are far from consensus (Aschemann-Witzel & Zielke, 2017). The change in the quantity of income can only reflect the change in the value of nominal income, and cannot truly reflect the income level and consumption level of residents, which belongs to the category of quantitative change. However, the income quality not only determines the income level but also reflects the degree of residents’ participation in economic activities. The change in income quality can truly reflect the improvement of their economic power and the increase in consumer purchasing power, which belongs to the category of qualitative change (Liu et al., 2019; Peng et al., 2019). Therefore, distinguishing from previous studies, this study takes from the quality of income, wondering if the optimization of income quality can effectively enhance consumers’ organic food purchase intention.

Environmental values are people’s internal beliefs, which are one of the fundamental factors influencing consumers’ ecological consumption behavior and can be transformed into green consumption behavior in certain contexts, but their direct transformation is inefficient and requires the help of other variables to facilitate the transformation (de Groot & Steg, 2007). Neuman (1986) proposed that values are psychological variables that influence consumers’ green consumption behavior, but their degree of influence is not very high. Hence, he proposed that mediating variables should be considered when studying the relationship between values and green consumption behavior. Lee et al. (2019) conducted a study between values, consumer perceived effectiveness, and green consumption behavior. He concluded that consumer altruism and ecospheric values significantly affect consumer perceived effectiveness, and consumer perceived effectiveness affects ecological purchase behavior.

Based on this, this study aims at exploring the relationship between income quality, environmental values, perceived consumer effectiveness, and organic food purchase intention. Concerns about income inequality and environmental pollution have been important aspects of achieving sustainable development goals. Efforts are still being made by economies to reduce income inequality and mitigate environmental degradation, and the promotion and use of organic food is one important way to reduce resource consumption and waste. By introducing a new variable of income quality, this study takes the extended VAB model as a research framework. In practice, this study can help the government, certification agencies, agriculture-related enterprises, and other subjects to formulate reasonable and effective management and marketing strategies for organic food from the perspective of consumers, which may expand the consumer market of organic food in China.

Literature Review and Research Hypothesis

Research Framework

Value-Attitude-Behavior (VAB) theory explains how values influence attitudes and buying behavior (Chryssohoidis & Krystallis, 2005). It states that the influence of values on particular behaviors works through attitudes toward the behavior (Homer & Kahle, 1988). VAB theory has been empirically supported in many sustainable consumption studies (e.g., McCarty & Shrum, 1994). VAB theory has also been found to help explain individuals’ green purchasing behavior (Cheung & To, 2019), environmentally responsible purchasing (Han et al., 2019), etc. In particular, the VAB theory has also been shown to be valid when discussing organic food purchases (Thøgersen et al., 2016). Therefore, the VAB theory is appropriate for this study.

Many studies have described environmental concerns as post-materialistic. They propose that the amount of income directly reported by residents can be one of the determinants of environmental concerns (Li & Chen, 2018). Thus, higher-income groups can expect to show higher environmental values (Inglehart, 1995). In previous studies, it has also been found that higher-income groups have more positive recycling behavior (Berger, 1997) and are more likely to purchase organic food (Yin et al., 2010). However, no studies have been conducted to integrate income quality into the role of environmental values and organic food purchases. Therefore, this study constructs a theoretical framework as shown in Figure 1.

Figure 1.

Hypothesis model.

Impact of Income Quality on Purchase Intention

Income adequacy, income growth, and income knowledge can reflect differences in income quality (Peng et al., 2019). Liu et al., (2019) explored the effects of income quality dimensions on farmers’ consumption levels using branded fresh meat as the study object. He found that income adequacy, income growth, and income knowledge significantly and positively affected the propensity to consume branded fresh meat.

Income adequacy refers to the quantitative characteristics of income, which is the material basis for purchase decisions. Darmon and Drewnowski (2015) compared differences in food security needs across income groups and found that higher income groups were more interested in food security and nutrition, while lower income groups were interested in having enough to eat. A sufficient amount of income can enhance consumers’ ability to pay, which in turn enhances their propensity to purchase organic food (Rana & Paul, 2017). Income growth refers to the stability of obtaining income and the possibility of future growth in the amount of income, which can change consumer tendencies. Persistent income theory suggests that it is the stable income component of income that can have a profound impact on consumption (Jappelli & Pistaferri, 2010). When the stability of income is higher, consumers have more confidence in future income growth and thus expand their consumption of organic food. Conversely, consumers with a lack of income stability tend to develop prudential characteristics, save preventively to protect their consumption needs, and are less inclined to consume organic food. Income knowledge refers to the level of consumer knowledge or skill embodied in income acquisition, which is the basis of the intellectual ability to purchase organic food. In general, consumers with a high level of expertise are more confident in their decisions and behavior (Simpson et al., 2012). Well-educated people will be more inclined to pursue organic food (Ghali-Zinoubi, 2021). Psychological accounts theory suggests that hard-earned income influences consumption attitudes and thus consumption due to differences in psychological weights (Tversky & Kahneman, 1981). In view of this, this study proposes the following hypotheses.

H1: There is a significant positive effect of income quality on the organic food purchase intention

The Mediating Role of Perceived Consumer Effectiveness

Perceived consumer effectiveness (PCE) refers to the extent to which consumers believe in the benefits (environmental/personal) of their organic food purchases (Lee et al., 2019). Many studies highlight that consumers’ positive influence on their behavioral outcomes promotes attitudes and reactions toward green products (e.g., Higueras-Castillo et al., 2019; Niedermeier et al., 2021). A study by Kautish et al. (2021) found that perceived consumer effectiveness had the greatest impact on behaviors related to choice behavior, even over demographic variables and other psychological variables. Consumers who are convinced that their environmentally responsible behavior will lead to positive outcomes are more likely to engage in such behavior to support their environmental concerns (Kim & Choi, 2005). Accordingly, in the context of organic food consumption, the more consumers believe that their organic food purchases will have various benefits, the more likely they are to purchase organic food. PCE is an individual’s belief in a particular situation (Kim & Choi, 2005). It is determined by knowledge as well as direct and indirect experience and varies according to individual abilities and individual resources (Tan, 2011). PCE can be seen as a mediator between individual resources (e.g., money, knowledge, and skills) and subsequent behavior. Once an individual has a higher valuation of the resources he or she has, he or she will feel less unfamiliar and fearful of new things and gain more confidence to approach and deal with them. Then, the consumer’s perceived effectiveness will increase (Lee et al., 2019). In the area of organic food consumption, we can speculate that individuals with high-income qualities are more confident that they can make a difference in the environmental situation and believe that they can afford to buy organic products more than consumers with low-income qualities. In view of this, this study proposes the following hypotheses.

H2: Perceived consumer effectiveness plays a mediating role between income quality and organic food purchase intention

The Mediating Role of Environmental Values

Values are the basis for guiding attitudes, judgments, and actions. They are critical to the generation of consumer purchase decisions. Hansen et al. (2018) have demonstrated that values can significantly influence consumers’ organic consumption behavior. Organic produce typically costs more, and therefore, consumers must look beyond single-consumer interests to the future interests of the group, such as cleaner air and water. Thus, consumers with environmental rather than self-directed values may be more willing to adopt green behaviors (Prakash et al., 2019). Consumers who place a high value on nature and the well-being of others may be more motivated to do their part to protect the environment.

An important proposition of post-industrial society theory and post-materialist values theory is that economic growth and the consequent increase in people’s standard of living leads to a transformation in people’s values, one important manifestation of which is a greater concern for the environment (Shao et al., 2018; Z. Wang, 2020). A possible corollary of this theory is that consumers with higher levels of income quality will be more likely to be motivated by their environmental values and thus will be more inclined to purchase organic food. In view of this, this study proposes the following hypotheses.

H3: Environmental values play a mediating role between income quality and organic food purchase intention

The Chain Mediating Role of Environmental Values and Perceived Consumer Effectiveness

Individual psychological factors are extremely complex and interact with each other. So modeling and analyzing them in the form of a mediating chain can lead to more realistic conclusions (Nagaraj, 2021). Neuman (1986) argues that values influence green consumption behavior and that there are mediating variables in between. Studies have been conducted to select attitudes, subjective norms, and perceived behavioral control of green consumption as mediating variables (Jaiswal & Kant, 2018), concluding that environmental values positively affect green consumption behavior through mediating variables. The study of Lee et al. (2019) showed that consumers with altruistic/ecological value orientation are more motivated to purchase green products, but the necessary condition is that the perceived consumer effectiveness is strong. Saleem et al. (2018) used perceived behavioral efficacy as a mediating variable and concluded that altruistic and ecological values significantly and positively act on ecological consumption behavior, while self-interested values negatively act on ecological consumption behavior. According to the self-regulation attitude theory, the affective stage is the stage in which consumers have positive or negative emotional reactions to organic food, and this stage is influenced by values. That is, when consumers have certain environmental values, the stronger the perceived consumer effectiveness, the stronger the correlation between environmental values and green consumption. In view of this, this study proposes the following hypothesis.

H4: Environmental values and perceived consumer effectiveness play a chain mediating role between income quality and organic food purchase intention

Study Design

Questionnaire Design

The questionnaire of this study is divided into two parts: the first part is the main part of the questionnaire, which includes the scales of each variable. The second part is the personal information of the respondents. The measurement items of each variable in the hypothesis model were taken from well-established scales that are widely used in the relevant literature. We modified appropriately according to expert opinions to better match organic food consumption situations. All scales were in the form of Likert 5-point scales.

The measurement of income quality draws on relevant studies by Liu et al. (2019) and Deng et al. (2020). It contains three dimensions: income adequacy, income growth, and income knowledge. Environmental values reflect people’s perceptions about the relationship between people and nature, and the measurement is based on the research scale of Saleem et al. (2018). Perceived consumer effectiveness was adapted from Kumar et al. (2021). The organic food purchase intention was adapted from Lee et al. (2019) (see Table 1).

Table 1.

Measurements and References.

Variables		Measurements	References
Incomequality	Income adequacy	The degree of satisfaction with your family’s savings in 2020/2021 (IA1)	Liu et al. (2019);Deng et al. (2020)
		The degree of satisfaction with the amount of your household’s income balance in 2020/2021 (IA2)
		IA3-The degree of satisfaction between income and costinput in 2020/2021 (IA3)
	Income growth	The changes in your household income in the last 3 years (IG1)
		The likelihood of long-term future income growth foryour family (IG2)
		The possibility for your family to expand revenue streamsin the future (IG3)
	Income knowledge	The knowledge and skill content of your current work (IK1)
		The number of times have you received relevant technicaltraining in the last three years (IK2)
		The number of members of your household workforcewith a license to practice (IK3)
Environmentalvalues		Respecting nature and protecting the environmentis important (EV1)	Saleem et al. (2018)
		Preventing pollution is important (EV2)
		Living in harmony with nature is important (EV3)
Perceived consumereffectiveness		Buying and using organic food can reduce the consumptionof natural resources (PCE1)	Kumar et al. (2021)
		Organic produce follows the laws of natural growth and hashigher nutritional value (PCE2)
		Buying and using organic produce means I’m doing the right thing (PCE3)
Purchase intention		The degree of possibility to buy organic food (PI1)	Lee et al. (2019)
		The degree of interest to buy organic food (PI2)
		The degree of possibility to recommend others tobuy organic food (PI3)

Questionnaire Pre-Test

Before the official questionnaire, a pre-survey was conducted to ensure the validity of the questionnaire. The questionnaires were distributed and collected from eligible friends and classmates through the WeChat platform, and a total of 50 questionnaires were collected. After that, we combined the interview method with the return visit to those who filled out the questionnaire to collect their feelings and suggestions. The reliability and validity of the questionnaire were analyzed using SPSS24.0 software, and the items with Cronbach’s α value less than .6 or KMO value less than 0.6 under each dimension were deleted. By observing the factor rotation component matrix, the measurement items with factor loadings less than 0.5 on each principal component were removed until the questionnaire structure could well explain each variable in the study. Finally, according to the opinions of the questionnaire fillers and the three Ph.D. supervisors, some ambiguous statements and measurement items with unclear expressions were adjusted and modified to form the final questionnaire.

Data Collection

No statistical differences were observed in the responses between the online questionnaires and those obtained from face-to-face questionnaires (Steffen et al., 2014). With the rapid development and popularity of mobile Internet technology, the sample bias faced by web-based survey methods is also rapidly decreasing (Windle et al., 2011). Moreover, web surveys can also alleviate to some extent the sample selection bias that may arise from the high rejection rate of traditional survey methods (Zhou et al., 2017). Thus, this study was collected online.

Following Yamane’s (1973) formula for calculating sample sizes, the suggested minimum sample size is 384, and in the actual research situation, the required sample size is larger than the minimum value. We collected the data in two sessions. The first time we used the Questionnaire Star platform, which was in August 2021. A total of 293 questionnaires were collected, and the remaining number of questionnaires with wrong answers and the number of questionnaires with too long/short answers were removed, resulting in a final sample size of 270. For the second time, we used the Credamo platform and collected 200 additional questionnaires, in July 2022. The number of questionnaires with too long/short response times and the number of questionnaires with continuous consistent responses was selected as “rejected” in the sample cleanup. Finally, the total number of samples was 450.

To ensure the integrity of the questionnaire, all questions were set as “mandatory” in both surveys. Only after completing the answers to these questions could the whole survey be completed. Also, to ensure the reliability of the questionnaire, we set the trap question in the questionnaire according to the method provided by Gao et al. (2016): “Please select the ‘blue’ option in the following four options.” Only those who answered correctly can be counted as valid questionnaires.

Analysis Method

Structural Equation Modeling is an important method for multivariate data analysis because it can consider and handle multiple independent variables simultaneously, and it can also address the case of unobservable latent variables (Wu, 2013). This study was divided into three segments to process the data for analysis. First, validation factor analysis was conducted using SPSS24.0 and AMOS24.0 to test the reliability and validity of the variables to ensure the goodness of fit of the structural model. Second, AMOS24.0 was used to conduct hypothesis testing of the structural model to verify the relationship between income quality, environmental values, perceived consumer effectiveness, and organic food purchase intention. Third, the bootstrapping method was used to test the mediating role of environmental values and perceived consumer effectiveness between income quality and organic food purchase intention, respectively.

Descriptive Statistical Analysis

Table 2 shows the descriptive statistics. According to the latest data from the seventh national census in China, the ratio of male to female was 51:48. There are more women than men in this survey. The possible reason for this is that women are the ones who are mainly in charge of purchases in a household (Chen et al., 2019). The age of the sample was mainly 19 to 59 (99.1%). Given that this study was conducted online, the percentage was higher than the census (63.35%). Additionally, the educational levels and family members of this sample are similar to those reported in earlier organic food purchase intention studies (e.g., J. Wang et al., 2020; Zheng, Zeng et al., 2022). The majority of the sample (72.0%) reported that there was child and elder in their home. In general, the sample of this study can well represent the organic food consumption group, which provides reliable data support to verify the research hypothesis.

Table 2.

Descriptive Statistics of Consumer Social Demographic Characteristics.

Demographic characteristic	Frequency	%
Sex
Male	158	35.1
Female	292	64.9
Age (years)
18 or below	1	0.2
19–28	185	41.1
29–45	176	39.1
46–59	85	18.9
60 or above	3	0.7
Educational level
Junior high school or below	5	1.1
High school (including secondary occupation)	13	2.9
College	63	14.0
Undergraduate	313	69.6
Master or above	56	12.4
Family member
2	20	4.4
3	202	44.9
4	124	27.6
More than 5	104	23.1
Is there a child under 12 years or an elder over 65 years?
Yes	324	72.0
No	126	28.0

Common Method Variance

The data in this study are cross-sectional, it is, therefore, important to control for the effects of common method variance (CMV) (Alcover et al., 2022). Both a priori and a posteriori strategies are used. In terms of a priori strategy, this study collected data in two periods and two platforms to reduce homoscedasticity variance (Podsakoff et al., 2012). In terms of a priori strategy, this study used the Harman single-factor test to verify, which is the most widely used method (Alcover et al., 2022). If CMV exists, the first unrotated factor on a principal component analysis on all items accounts for more than 50% of the variance. We performed unrotated principal component analysis on all question items of the study questionnaire and found that the first principal component explained only 36.60% of the variance, which is below the 50% criterion (Podsakoff & Organ, 1986). Therefore, common method variance does not affect the study results.

Measurement Model Testing

The reliability of the questionnaire was tested by Composite Reliability (CR). Values above .7 are considered acceptable (Wu, 2013). Table 2 shows the result. CR values were all greater than .7, representing sufficient reliability and internal consistency of each dimensional indicator. We also test the second-order measurement scale. As shown in Table 3, the CR values of IQ was .787, which is greater than .7. The coefficients were all significant at the .001 level. Therefore, the scales have good convergent validity.

Table 3.

Results of Measurement Model Analysis.

Variables	Items	Unstandardized	SE	Z-value	p	Stdandardized	CR	AVE
IQ	IA	1.000				0.876	.787	.559
	IG	1.026	0.115	8.898	***	0.768
	IK	0.592	0.079	7.503	***	0.567
IA	IA1	1.000				0.772	.898	.747
	IA2	1.174	0.055	21.343	***	0.938
	IA3	1.231	0.061	20.200	***	0.875
IG	IG1	1.000				0.915	.840	.645
	IG2	0.930	0.042	22.173	***	0.871
	IG3	0.586	0.044	13.231	***	0.581
IK	IK1	1.000				0.670	.817	.600
	IK2	0.944	0.069	13.633	***	0.798
	IK3	1.070	0.078	13.786	***	0.845
PCE	PCE1	1.000				0.850	.824	.610
	PCE2	0.877	0.061	14.410	***	0.739
	PCE3	0.898	0.062	14.508	***	0.749
EV	EV1	1.000				0.809	.795	.564
	EV2	0.883	0.070	12.619	***	0.713
	EV3	0.924	0.073	12.704	***	0.728
PI	PI1	1.000				0.846	.846	.649
	PI2	0.908	0.061	14.927	***	0.683
	PI3	1.098	0.064	17.225	***	0.874

Note. IK standardized factor loading (0.595) was close to 0.6 and did not affect the AVE value, so it was retained. CR = composite reliability; AVE = average variance extracted; IQ = income quality; IA = income adequacy; IG = income growth; IK = income knowledge; PCE = perceived consumer effectiveness; EV = environmental values; PI = purchase intention.

***

p < .001.

The validity measures were tested by convergent validity and differential validity, where convergent validity was mainly reflected by standardized factor loadings, Z-value, and Average Variance Extracted (AVE). Values above .5 are acceptable (Fornell & Larcker, 1981). The results showed that the standardized factor loadings were all greater than 0.6 and significant, all AVEs were greater than .5, representing a high convergent validity of the scale. Meanwhile, the correlation coefficients between any two variables were smaller than the square root of the AVE of each variable itself (Table 4), so the scale had good discriminant validity, which laid the foundation for the later analysis of the structural model.

Table 4.

Results of Discriminant Validity Test.

Variables	AVE	IQ	PI	PCE	EV
IQ	.559	.748
PI	.649	.627	.806
PCE	.610	.542	.639	.781
EV	.564	.200	.325	.346	.751

Note. The items on the diagonal represent the square roots of the AVE; off-diagonal elements are the correlation estimates.

Structural Model Testing

Table 5 shows the fit indicators of the model all meet the criteria, indicating that the model fits well.

Table 5.

Fitting Results of the Model.

Index	Criteria	Model fit	Result
χ²	The smaller the better	100.244
df	The bigger the better	48
χ²/df	<3	2.088	Ideal
GFI	>0.9	0.965	Ideal
AGFI	>0.9	0.942	Ideal
RMSEA	<0.05	0.049	Ideal
CFI	>0.9	0.977	Ideal
TLI (NNFI)	>0.9	0.968	Ideal

Note. GFI = goodness of fit index; AGFI = adjusted goodness of fit index; RMSEA = root mean square error of approximation; CFI = comparative fit index; TLI = Tucker-Lewis index.

As shown in Table 6, the results of hypothesis testing (Table 6) showed that all hypotheses were supported. Specifically, income quality had a significant positive effect on environmental values, perceived consumer effectiveness, and organic food purchase intention (β = .222, p < .001; β = .492, p < .001; β = .421, p < .001, respectively). Environmental values had a significant positive effect on perceived consumer effectiveness (β = .236, p < .001) and organic food purchase intention (β = .102, p < .05). Perceived consumer effectiveness had a significant positive effect on organic food purchase intention (β = .374, p < .001). Figure 2 shows the path coefficients.

Figure 2.

Hypothesis model with path coefficients.

Table 6.

Results of the Hypothesis Test.

DV	IV	SE	CR	p	β	Findings
EV	IQ	0.071	3.676	***	.222	Supported
PCE	IQ	0.090	7.387	***	.492	Supported
PCE	EV	0.063	4.358	***	.236	Supported
PI	IQ	0.103	6.444	***	.421	Supported
	EV	0.064	2.134	*	.102	Supported
	PCE	0.071	6.145	***	.374	Supported

p < .05. ***p < .001.

Mediation Test

For testing the mediating effect, the causal method of Baron and Kenny (1986) and Sobel (1982) were used in the early days. However, these two methods have problems such as low statistical test power, making type I errors, and the results of multiplying two lines of mediating effects are not normally distributed, etc. (MacKinnon et al., 1998). MacKinnon (2012) suggested the bootstrap method, which uses the confidence interval (CI) to solve the above problems.

Bootstrap is the most widely used method for directly testing the product of coefficients (Wen et al., 2022). Bootstrap has several sampling schemes, one of which is a simple method of repeatedly sampling from a given sample to produce many samples, that is, treating the original sample as the total bootstrap, and repeatedly sampling from this total bootstrap to obtain a bootstrap sample similar to the original sample. A more specific explanation can be viewed by C. Jiang and Li (2015). The SPSS 24.0 used in this study already has simple instructions to call the bootstrap method to calculate the confidence interval (CI) for the product of coefficients. Judgment was made based on the confidence interval (CI) of the indirect effect, and if the CI did not contain 0, the original hypothesis was rejected, representing that the indirect effect was not 0 and the mediating effect existed (Hayes, 2009). Therefore, we referred to Hayes (2009) and used Model 6 in Process 4.0 to examine the mediation effect, setting bootstrap to 5000 times with 95% confidence interval. The results are shown in Table 7.

Table 7.

Results of Mediating Effect Test.

Paths	β	BootSE	LLCL	ULCL
IQ→PCE→PI	.146	0.031	0.086	0.207
IQ→EV→PI	.023	0.013	0.000	0.052
IQ→EV→PCE→PI	.016	0.007	0.005	0.030
Total effect	.578	0.043	0.494	0.662
Indirect effects	.185	0.032	0.121	0.247
Direct effects	.394	0.043	0.309	0.478

As shown in Table 7, the indirect effect was present and significant, indicating the existence of a mediating effect. The direct effect was smaller than the total effect and significant, indicating a partial mediating effect. The effect of perceived consumer effectiveness was 0.146, with a confidence interval [0.086, 0.207]. That is, perceived consumer effectiveness partially mediates the relationship between income quality and organic food purchase intention, and H2 was supported. The effect for environmental values was 0.023, with a confidence interval [0.000, 0.052]. Thus, H3 was not supported. The effect of environmental values and perceived consumer effectiveness as chain mediating variables was 0.016 with a confidence interval containing no 0. Therefore, there is a chain mediating role of environmental values and perceived consumer effectiveness in the relationship between income quality and organic food purchase intention, and H4 was supported.

Discussion

Research Findings

This study constructed a chain mediation model of income quality on organic food purchase intention based on an extended Value—Attitude—Behavior theory. The results showed that:

First, income quality significantly and positively affects organic food purchase intention. The higher the level of income quality, the more likely consumers are to buy organic food. While income levels in China have increased rapidly, improvements like income have lagged significantly, thus largely offsetting the consumption-driving effect of income growth (J. Jiang, 2019). However, the results of this study suggest an increasing role of income quality (e.g., income adequacy, income growth, and income knowledge). Therefore, while increasing the absolute income of the population, measures should be taken to reduce the income gap of the population. Meanwhile, the results of the model analysis show that the dimensions of income adequacy (0.876) and income growth (0.768) are more effective in improving the quality of income, and income knowledge (0.567) is followed. It indicates that accelerating economic growth and increasing residents’ income at this stage is still an important way to optimize income levels and thus promote the consumption of organic food in China. However, the role of income knowledge in promoting the consumption of organic food should not be neglected. Attention should be paid to improving consumers’ income growth and knowledge by improving their employment environment and social security and increasing the contribution of science and technology.

Second, income quality also indirectly affects the organic food purchase intention through perceived consumer effectiveness. The mediating effect of perceived consumer effectiveness accounted for 78.82% of the total indirect effect. Therefore, it is important to pay due attention to the facilitative effect of perceived consumer effectiveness. According to social cognitive theory and self-regulation attitude theory, perceived consumer effectiveness explains how consumers perceive the benefits and utility they receive from purchasing organic food. It is an overall assessment of the net benefits (Hansmann et al., 2020). As an important mediating variable in the consumer purchase process, perceived consumer effectiveness can serve as a signal of consumer judgment and a key antecedent of purchase intentions (Sharma & Foropon, 2019).

Third, the direct effect of income quality on environmental values is significantly positive, but the mediating effect of environmental values on income quality and purchase intention is not significant. Unlike the study by Z. Wang (2020), the results of this study support the study by Shao et al. (2018) by suggesting that consumers with high-income quality have stronger public awareness. The theoretical explanation may be derived from the “Affluent Society” (Y. Wang, 2016). With the continuous optimization of income, people get rid of the environmental insecurity and material shortage of the past, thus creating values that go beyond material needs. However, environmental values do not directly mediate the relationship between income quality and organic food purchase intention. Further, we found that the chain mediation of environmental values and perceived consumer effectiveness between income quality and organic food purchase intention is significant. That is, consumers who have environmental values are more likely to purchase organic food, but only when they have positive PCE beliefs. Values cannot be directly translated but must be perceived as effective by consumers. The results of this study support the studies of Neuman (1986) and Lee et al. (2019) that there are mediating variables between values on organic consumption behavior. Most scholars currently choose to consider attitudes, subjective norms, and perceived behavioral control as mediating variables (Paço et al., 2019), but the results of this study suggest that perceived consumer effectiveness is also a mediating variable with explanatory power.

Theoretical Contributions

The theoretical contributions of this study are in the following two aspects.

First, this study introduces income quality into the study of organic food purchase behavior, which is its new practice and exploration in the field of consumer behavior. Although previous studies have recognized the important influence of income level on organic food purchase intention, they have neglected the qualitative characteristics of income. The results of this study show that all dimensions of income quality have good reliability and validity, indicating that income quality has good applicability in the field of studying organic food consumption and exploring the way for the subsequent wide application of this scale.

Second, this study innovatively incorporates income quality, environmental values, and perceived consumer effectiveness into the theoretical framework of organic food purchase intention, using the extend Value—Attitude—Behavior theory. This study suggests that income quality is an antecedent variable of perceived consumer effectiveness, environmental values, and purchase intention and that the role of environmental values depends on perceived consumer effectiveness. Previous studies have focused on the formation process of purchase intention from a single psychological perspective, and have not included economic factors such as price and income, which directly affect consumption decisions, into the research framework, somewhat reducing the explanatory power of the model. The concept of income quality can effectively integrate the two and further explain the mechanism of organic food purchase intention.

Practical Implications

In light of the findings of this study, it is recommended that the focus is primarily on optimizing the quality of the resident’s income and improving consumer perceived effectiveness. On the one hand, the Chinese government should vigorously raise the income level of the population. On the other hand, the quality of residents’ income will be improved by improving employment, improving social security and insurance policies, and promoting structural reform on the supply side. In addition, marketers can emphasize the benefits of organic food for personal health and for the environment to positively influence consumers’ perceived effectiveness and purchase intentions for the products offered. Finally, environmental values education has to be relevant to the individual. It is difficult to translate consumers’ perceptions into actual behavior if they are only educated on their perceptions without stirring up their emotional resonance. It should be able to inspire positive attitudes in consumers, which in turn will enhance their perceived effectiveness.

Research Limitations and Future Research Directions

There are some limitations in this study, and these limitations are where future research needs further attention. First, this study only measured consumers’ purchase intentions, and there is a large gap between intentions and behaviors. Future research can be measured by actual purchase behavior. Secondly, this study only considered the effect of different dimensions of income quality, and future studies can classify income quality into different levels, such as “low level, medium level, and high level” for verification and comparison. Finally, based on the present study, scholars can try to introduce other variables into the model to make the influence of income quality on organic food purchase behavior more complete and reliable.

Conclusion

While the traditional income-consumption theory suggests that the quantity of income determines the ability to pay for consumption, the expansion of income has given it a new explanatory power. Income not only contributes to the increase of consumption capacity in quantitative terms but also has a positive effect on its qualitative characteristics. The results of this study show that income quality has a significant positive effect on the purchase intention of organic food, that perceived consumer effectiveness plays a significant mediating role between income quality and purchase intention and shows a significant chain mediation together with environmental values. Therefore, measures should be taken to optimize the quality of Chinese consumers' income. More critically, it is crucial to focus on influencing consumer perceived effectiveness to better motivate consumers with environmental values to purchase organic food.

This research was conducted while Qiuqin Zheng was at Fujian Agriculture and Forestry University. She is now at Fujian Business University.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This work was supported by The National Social Science Foundation of China (19BJY048).

ORCID iD

Qiuqin Zheng

Data Availability Statement

The data used for this study are available upon request from the first author.

References

Alcover

C. -M.

Salgado

Nazar

Ramírez-Vielma

González-Suhr

(2022). Job insecurity, financial threat, and mental health in the COVID-19 context: The moderating role of the support network. SAGE Open, 12(3), 21582440221121048.

Yan

Zhang

(2022). Effect of income quality on farmers’ conscious adoption behavior of straw returning technology: Analysis of a moderated mediation model. Chinese Journal of Agricultural Resources and Regional Planning, 6, 162–172.

Aschemann-Witzel

Zielke

(2017). Can’t buy me green? A review of consumer perceptions of and behavior toward the price of organic food. Journal of Consumer Affairs, 51(1), 211–251. https://doi.org/10.1111/joca.12092

Baron

R. M.

Kenny

D. A.

(1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and social Psychology, 51(6), 1173–1182. https://doi.org/10.1037//0022-3514.51.6.1173

Berger

I. E.

(1997). The demographics of recycling and the structure of environmental behavior. Environment and Behavior, 29(4), 515–531.

Chen

Wang

Yin

Han

(2019). Chinese consumer trust and preferences for organic labels from different regions. British Food Journal, 121(7), 1521–1535. https://doi.org/10.1108/bfj-02-2018-0128

Cheung

M. F.

W. M.

(2019). An extended model of value-attitude-behavior to explain Chinese consumers’ green purchase behavior. Journal of Retailing and Consumer Services, 50, 145–153.

Chryssohoidis

G. M.

Krystallis

(2005). Organic consumers’ personal values research: Testing and validating the list of values (LOV) scale and implementing a value-based segmentation task. Food Quality and Preference, 16(7), 585–599.

Darmon

Drewnowski

(2015). Contribution of food prices and diet cost to socioeconomic disparities in diet quality and health: A systematic review and analysis. Nutrition Reviews, 73(10), 643–660. https://doi.org/10.1093/nutrit/nuv027

10.

de Groot

J. I. M.

Steg

(2007). Value orientations to explain beliefs related to environmental significant behavior. Environment and Behavior, 40(3), 330–354. https://doi.org/10.1177/0013916506297831

11.

Deng

Zhao

Kong

(2020). Research on “entrepreneurial intention” of farmers in western China from the perspective of income quality. Research on Economics and Management, 41(5), 33–43.

12.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

13.

Gao

House

L. A.

Xie

(2016). Online survey data quality and its implication for willingness-to-pay: A cross-country comparison. Canadian Journal of Agricultural Economics/Revue Canadienne D’agroeconomie, 64(2), 199–221.

14.

Ghali-Zinoubi

(2021). On linking socioeconomic status to consumer willingness to buy and pay for organic food. Journal of Food Science and Technology, 58(3), 1042–1050. https://doi.org/10.1007/s13197-020-04618-9

15.

Han

Hwang

Lee

M. J.

Kim

(2019). Word-of-mouth, buying, and sacrifice intentions for eco-cruises: Exploring the function of norm activation and value-attitude-behavior. Tourism Management, 70, 430–443.

16.

Hansen

Sørensen

M. I.

Eriksen

M. -L. R.

(2018). How the interplay between consumer motivations and values influences organic food identity and behavior. Food Policy, 74, 39–52. https://doi.org/10.1016/j.foodpol.2017.11.003

17.

Hansmann

Baur

Binder

C. R.

(2020). Increasing organic food consumption: An integrating model of drivers and barriers. Journal of Cleaner Production, 275, 123058. https://doi.org/10.1016/j.jclepro.2020.123058

18.

Hayes

A. F.

(2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76(4), 408–420. https://doi.org/10.1080/03637750903310360

19.

Higueras-Castillo

Liébana-Cabanillas

F. J.

Muñoz-Leiva

García-Maroto

(2019). Evaluating consumer attitudes toward electromobility and the moderating effect of perceived consumer effectiveness. Journal of Retailing and Consumer Services, 51, 387–398. https://doi.org/10.1016/j.jretconser.2019.07.006

20.

Homer

P. M.

Kahle

L. R.

(1988). A structural equation test of the value-attitude-behavior hierarchy. Journal of Personality and Social Psychology, 54(4), 638.

21.

Inglehart

(1995). Public support for environmental protection: Objective problems and subjective values in 43 societies. Political Science & Politics, 28(1), 57–72.

22.

Jaiswal

Kant

(2018). Green purchasing behaviour: A conceptual framework and empirical investigation of Indian consumers. Journal of Retailing and Consumer Services, 41, 60–69. https://doi.org/10.1016/j.jretconser.2017.11.008

23.

Janssen

(2018). Determinants of organic food purchases: Evidence from household panel data. Food Quality and Preference, 68, 19–28. https://doi.org/10.1016/j.foodqual.2018.02.002

24.

Jappelli

Pistaferri

(2010). The consumption response to income changes. Annual Review of Economics, 2(1), 479–506.

25.

Jiang

(2015). Mediation analysis and the application of bootstrap in mediation analysis. Psychological Exploration, 35(5), 458–463.

26.

Jiang

(2019). The logic and path of constructing high quality income distribution system in the new era. Journal of Xiangtan University (Philosophy and Social Sciences), 43(4), 64–68.

27.

Kakwani

Wang

Xue

Zhan

(2022). Growth and common prosperity in China. China & World Economy, 30(1), 28–57.

28.

Kautish

Sharma

Mangla

S. K.

Jabeen

Awan

(2021). Understanding choice behavior towards plastic consumption: An emerging market investigation. Resources, Conservation and Recycling, 174, 105828. https://doi.org/10.1016/j.resconrec.2021.105828

29.

Kim

Choi

S. M.

(2005). Antecedents of green purchase behavior: An examination of collectivism, environmental concern, and PCE. ACR North American Advances, 32, 592–599.

30.

Kong

Wang

(2013). Conception about income quality of migrant workers. Issues in Agricultural Economy, 34(6), 55–60.

31.

Kumar

Prakash

Kumar

(2021). Does environmentally responsible purchase intention matter for consumers? A predictive sustainable model developed through an empirical study. Journal of Retailing and Consumer Services, 58, 102270. https://doi.org/10.1016/j.jretconser.2020.102270

32.

Lee

Y. -K.

Kim

M. -s.

Katz-Gerro

Kim

(2019). Personal values, perceived consumer effectiveness and demographic on green purchasing behavior of Korean consumers. Environmental Engineering and Management Journal, 18(2), 349–358.

33.

Lee

C. -H.

Lin

Y. -T.

Liu

C. -W.

(2020). Chinese consumers’ willingness to pay for organic foods: A conceptual review. International Food and Agribusiness Management Review, 23(2), 173–188. https://doi.org/10.22434/ifamr2019.0037

34.

Liu

Zheng

Cao

(2021). An analysis of factors affecting selection of organic food: Perception of consumers in China regarding weak signals. Appetite, 161, 105145. https://doi.org/10.1016/j.appet.2021.105145

35.

Liu

Wang

Kong

(2019). Income quality, safety consumption consciousness and farmers’ consumption level of brand fresh meat. Journal of Agro-Forestry Economics and Management, 18(1), 88–100.

36.

Chen

(2018). Absolute income, relative income and environmental concern: Evidence from different regions in China. Journal of Cleaner Production, 187, 9–17.

37.

Luo

Chen

(2020). Family life cycle, income quality and rural household consumption structure: An analysis based on the family life cycle model from the perspective of children’s heterogeneity. Chinese Rural Economy, 8, 85–105.

38.

MacKinnon

D. P.

(2012). Introduction to statistical mediation analysis. Routledge.

39.

MacKinnon

D. P.

Lockwood

C. M.

Hoffman

(1998). A new method to test for mediation [Paper presentation]. The annual meeting of the Society for Prevention Research, Park City, UT.

40.

McCarty

J. A.

Shrum

(1994). The recycling of solid wastes: Personal values, value orientations, and attitudes about recycling as antecedents of recycling behavior. Journal of Business Research, 30(1), 53–62.

41.

Nagaraj

(2021). Role of consumer health consciousness, food safety & attitude on organic food purchase in emerging market: A serial mediation model. Journal of Retailing and Consumer Services, 59, 102423. https://doi.org/10.1016/j.jretconser.2020.102423

42.

Neuman

(1986). Personal values and commitment to energy conservation. Environment & Behavior, 18(1), 53–74.

43.

Niedermeier

Emberger-Klein

Menrad

(2021). Drivers and barriers for purchasing green fast-moving consumer goods: A study of consumer preferences of glue sticks in Germany. Journal of Cleaner Production, 284, 124804. https://doi.org/10.1016/j.jclepro.2020.124804

44.

Paço

A. d.

Shiel

Alves

(2019). A new model for testing green consumer behaviour. Journal of Cleaner Production, 207, 998–1006. https://doi.org/10.1016/j.jclepro.2018.10.105

45.

Peng

Kong

(2019). Income quality and its impact on farm household’s entrepreneurial decision——empirical evidence of 1373 farm households in Shangdong, Henan, Shaanxi, Gansu. Journal of Agrotechnical Economic, 12, 56–67.

46.

Podsakoff

P. M.

MacKenzie

S. B.

Podsakoff

N. P.

(2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63(1), 539–569.

47.

Podsakoff

P. M.

Organ

D. W.

(1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531–544.

48.

Prakash

Choudhary

Kumar

Garza-Reyes

J. A.

Khan

S. A. R.

Panda

T. K.

(2019). Do altruistic and egoistic values influence consumers’ attitudes and purchase intentions towards eco-friendly packaged products? An empirical investigation. Journal of Retailing and Consumer Services, 50, 163–169. https://doi.org/10.1016/j.jretconser.2019.05.011

49.

Rana

Paul

(2017). Consumer behavior and purchase intention for organic food: A review and research agenda. Journal of Retailing and Consumer Services, 38, 157–165.

50.

Saleem

M. A.

Eagle

Yaseen

Low

(2018). The power of spirituality. Asia Pacific Journal of Marketing and Logistics, 30(4), 867–888. https://doi.org/10.1108/apjml-10-2017-0259

51.

Shao

Tian

Fan

(2018). Do the rich have stronger willingness to pay for environmental protection? New evidence from a survey in China. World Development, 105, 83–94. https://doi.org/10.1016/j.worlddev.2017.12.033

52.

Sharma

Foropon

(2019). Green product attributes and green purchase behavior. Management Decision, 57(4), 1018–1042. https://doi.org/10.1108/md-10-2018-1092

53.

Simpson

J. A.

Griskevicius

Rothman

A. J.

(2012). Consumer decisions in relationships. Journal of Consumer Psychology, 22(3), 304–314.

54.

Sobel

M. E.

(1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312.

55.

Steffen

M. W.

Murad

M. H.

Hays

J. T.

Newcomb

R. D.

Molella

R. G.

Cha

S. S.

Hagen

P. T.

(2014). Self-report of tobacco use status: comparison of paper-based questionnaire, online questionnaire, and direct face-to-face interview–implications for meaningful use. Population Health Management, 17(3), 185–189. https://doi.org/10.1089/pop.2013.0051

56.

Tan

B. C.

(2011). The roles of knowledge, threat, and PCE on green purchase behaviour. International Journal of Business and Management, 6(12), 14–27.

57.

Tandon

Dhir

Kaur

Kushwah

Salo

(2020). Why do people buy organic food? The moderating role of environmental concerns and trust. Journal of Retailing and Consumer Services, 57, 102247. https://doi.org/10.1016/j.jretconser.2020.102247

58.

Thøgersen

Zhou

Huang

(2016). How stable is the value basis for organic food consumption in China? Journal of Cleaner Production, 134, 214–224.

59.

Tversky

Kahneman

(1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453--458.

60.

Wang

(2016). Food safety, consumer income and efficient eco-agriculture development. Contemporary Economic Management, 38(1), 23–30.

61.

Wang

Pham

T. L.

Dang

V. T.

(2020). Environmental consciousness and organic food purchase intention: A moderated mediation model of perceived food quality and price sensitivity. International Journal of Environmental Research and Public Health, 17(3), 850.

62.

Wang

(2010). Analysis of the impact of the quality of income on farmers’ consumption. Chinese Rural Economy, 4, 38–47.

63.

Wang

(2016). Social stratification, materialism, post-materialism and consumption values. Asia Pacific Journal of Marketing and Logistics, 28(4), 580–593. https://doi.org/10.1108/apjml-10-2015-0149

64.

Wang

Zhu

Chu

(2017). Organic vs. non-organic food products: Credence and price competition. Sustainability, 9(4), 545. https://doi.org/10.3390/su9040545

65.

Wang

(2020). Do high income earners care more about the environment? Empirical study based on survey data. Lanzhou Academic Journal, 10, 143–152.

66.

Wen

Fang

Xie

Ouyang

(2022). Methodological research on mediation effects in China’s mainland. Advances in Psychological Science, 30(8), 1692–1702.

67.

Willer

Lernoud

(2020). The world of organic agriculture. Statistics and emerging trends 2020. Research Institute of Organic Agriculture FiBL and IFOAM Organics International.

68.

Windle

Bennett

K. M.

Noyes

(2011). A methodological review of resilience measurement scales. Health and Quality of Life Outcomes, 9(1), 8. https://doi.org/10.1186/1477-7525-9-8

69.

(2013). Structural equation modeling: Advanced practice of Amos. Chongqing University Press.

70.

Yamane

(1973). Statistics: An introductory analysis-3. Harper and Row.

71.

Yang

Chen

Lin

Han

Chen

Zheng

Gao

Liu

(2021). Chinese consumer preferences for organic labels on Oolong tea: Evidence from a choice experiment. International Food and Agribusiness Management Review, 24(3), 545–561. https://doi.org/10.22434/ifamr2020.0113

72.

Yin

Chen

(2010). Consumers’ purchase intention of organic food in China. Journal of the Science of Food and Agriculture, 90(8), 1361–1367. https://doi.org/10.1002/jsfa.3936

73.

Zheng

Wen

Xiu

Yang

Chen

(2022). Can the part replace the whole? A choice experiment on organic and pesticide-free labels. Foods, 11(17), 2564.

74.

Zheng

Zeng

Xiu

Chen

(2022). Pull the emotional trigger or the rational string? A multi-group analysis of organic food consumption. Foods, 11(10), 1375. https://doi.org/10.3390/foods11101375

75.

Zhou

Liu

Mao

(2017). Habit spillovers or induced awareness: Willingness to pay for eco-labels of rice in China. Food Policy, 71, 62–73.

Income Quality and Organic Food Purchase Intention: The Chain Mediating Role of Environmental Value,Perceived Consumer Effectiveness

Abstract

Plain Language Summary

Keywords

Introduction

Literature Review and Research Hypothesis

Research Framework

Impact of Income Quality on Purchase Intention

The Mediating Role of Perceived Consumer Effectiveness

The Mediating Role of Environmental Values

The Chain Mediating Role of Environmental Values and Perceived Consumer Effectiveness

Study Design

Questionnaire Design

Questionnaire Pre-Test

Data Collection

Analysis Method

Descriptive Statistical Analysis

Common Method Variance

Measurement Model Testing

Structural Model Testing

Mediation Test

Discussion

Research Findings

Theoretical Contributions

Practical Implications

Research Limitations and Future Research Directions

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

Data Availability Statement

References