Thresholds of Sustainability: Necessary and Sufficient Conditions for Green Buying Behavior

Abstract

This study investigates the determinants of eco-buying behavior by drawing from the Theory of Planned Behavior (TPB) and considering green consumption values and prosocial attitudes. Using a cross-sectional survey of 436 adults in the UK and employing a two-step analysis with Partial Least Squares Structural Equation Modelling (PLS-SEM) and Necessary Condition Analysis (NCA), this research examines both sufficient and necessary conditions for sustainable purchasing behavior. The findings reveal that green consumption values and social influence are positively associated with green buying behavior, with green consumption values exerting the strongest influence. Conversely, prosocial attitudes and perceived behavioral control show no significant direct effects, yet age moderates the relationship between prosocial attitudes and green buying behavior. Moreover, NCA identifies green consumption values and perceived behavioral control as necessary conditions at specific thresholds, underscoring their importance in fostering high levels of green buying behavior. Additionally, a logarithmic relationship is observed between green consumption values and green buying behavior, suggesting diminishing returns at higher levels of green consumption values. Notably, the influence of green consumption values on green buying behavior is stronger among older individuals (35+), highlighting age-based differences in sustainable consumption. By integrating both sufficient and necessary conditions, this research addresses the attitude-behavior gap in sustainable consumption, offering novel insights into the roles of intrinsic values and social influences. These findings challenge established constructs like perceived behavioral control and highlight the relevance of advanced analytical methods in sustainable consumer research. The study contributes theoretical insights and practical implications for sustainable marketing strategies targeting value-driven consumers.

1. Introduction

Environmental challenges loom larger than ever, demanding transformative changes in how societies consume and produce. With escalating climate crises, biodiversity loss, and resource depletion, the United Nations has declared sustainable consumption patterns not just a goal but a necessity [1]. Yet, despite an era marked by heightened environmental awareness and rising ethical consumerism, a perplexing paradox persists: consumers fail to translate eco-conscious attitudes into action [2,3,4,5].

Why do intentions falter in the face of action? For businesses striving to promote sustainability, this “attitude-behavior gap” is not merely an academic curiosity; it represents a formidable challenge to survival [6,7]. Eco-conscious brands struggle to persuade consumers to adopt greener alternatives, particularly when these involve perceived trade-offs in cost, convenience, or performance [8,9,10,11]. This gap is even more pronounced in industries like fashion, where sustainable options face skepticism about quality and authenticity [10]. Without actionable insights into bridging this gap, sustainable business models risk stagnation, and the global vision of a sustainable future remains precariously out of reach.

To address this conundrum, this study explores the psychological and social mechanisms that drive green purchasing behavior. Existing research has utilized the Theory of Planned Behavior (TPB) as a framework for understanding intention-driven actions, yet its limitations have been highlighted in sustainability contexts [8,12,13,14,15]. The limitations lead to the following questions: Are intentions enough, or are deeper, value-based motivators at play? What role do societal pressures or altruistic inclinations hold in influencing eco-friendly behavior? Building on these questions, echoing the calls for further research [3,16], we extend the TPB by incorporating green consumption values (GRCV), capturing deeply held environmental convictions and prosocial attitudes, reflecting altruistic motivations. These theoretical augmentations aim to unravel the complexities of green-buying behavior, shifting the focus from merely explaining behavior to uncovering actionable levers for transformative change [17,18].

To address this methodologically, we implement a comprehensive two-step analytical strategy combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA). While PLS-SEM is well-established in consumer behavior research for assessing the strength and significance of relationships among variables, both linear and non-linear associations, it primarily operates within sufficiency logic, identifying factors that can lead to an outcome. However, sufficiency does not inherently imply necessity. This is where NCA provides an important complement. NCA, as introduced by Dul [19], shifts the analytical lens by identifying necessary but not sufficient conditions (i.e., those without which a desired outcome cannot occur). This approach uncovers critical thresholds of predictor variables that must be met for the outcome (e.g., green buying behavior) to materialize, thereby offering insights into constraints and bottlenecks that are invisible in traditional regression-based techniques [19,20].

Although relatively new, NCA has gained significant attention in management and marketing research for its ability to pinpoint critical thresholds for desired behavioral outcomes [20]. Our dual-method approach offers a holistic view, integrating insights into both sufficient and necessary conditions for sustainable consumption. By identifying bottleneck conditions and critical thresholds, NCA illuminates the non-compensatory nature of certain factors, such as the indispensability of green consumption values for fostering green purchasing decisions. These insights provide actionable guidance for designing interventions and policies in sustainability contexts. Furthermore, by identifying tipping points and zones of diminishing returns, NCA enables more targeted, efficient intervention design and policy-making, ensuring resources are focused where they are most critical. This dual-method approach responds to the complexity of pro-environmental behavior and helps advance theory and practice by acknowledging both enabling and constraining conditions.

The implications of this research extend to practical strategic questions: If intrinsic values like GRCV can indeed outweigh societal pressures, what communication strategies could brands adopt to align their messaging with these motivators? If certain thresholds of perceived control are essential but not sufficient, how might interventions be designed to empower consumers without undermining their intrinsic motivations? By addressing these questions with robust empirical methods, this study aims to advance the theoretical discourse on sustainable consumption while providing evidence-based recommendations for fostering sustainable behaviors.

2. Conceptual Framework

Given the significance of sustainable behavior, an extensive body of literature seeking to understand its determinants has emerged. A consistent finding across these studies is the discrepancy between positive consumer attitudes towards environmental issues and observed eco-friendly or sustainable behavior. Despite expressing environmental concerns, consumers are less likely to go the extra mile to buy eco-friendly products [7,16,21,22,23]. This gap has been attributed to multiple factors, including the higher price points associated with green products, their limited market availability, and occasionally their inferior performance compared to conventional alternatives [11,18,22,24,25]. These barriers collectively impede the translation of pro-environmental attitudes into corresponding purchasing behaviors. The following sections discuss each key variable in our conceptual framework and develop the research hypotheses that guide this investigation.

2.1. Theory of Planned Behavior (TPB)

The Theory of Planned Behavior (TPB), an evolution of Fishbein and Ajzen’s [26] Theory of Reasoned Action (TRA), incorporates perceived behavioral control (PBEC) and extends to scenarios where consumers face constraints on their actions, beyond mere intentions [27,28]. This fits well with sustainable consumption behavior, and TPB is widely applied in related contexts such as organic food purchases [29,30], recycling behaviors [31,32], eco-fashion buying [33,34], electronic products [35], and electric vehicle adoption [36,37,38]. Moreover, studies that consider general green purchase intentions have also utilized TPB as a theoretical framework to understand sustainable consumer behavior [15,39,40,41,42,43,44].

A core element of TPB is subjective norms (SUBN). Subjective norms typically manifest as social influence and reflect perceived social pressures regarding a specific behavior. This influence materializes especially from close contacts, such as family, friends, and peers [27]. Studies on green buying across diverse contexts, including organic food consumption, sustainable clothing purchase, and electric vehicle adoption, have consistently demonstrated a positive association between SUBN and behavioral intentions [2,29,30,33,37,40,42,43,45]. The consistent empirical support for this relationship across varied sustainable consumption domains underscores the importance of social influence mechanisms in promoting environmentally responsible purchasing. Drawing from this rich body of research, we hypothesize the following:

Hypothesis 1:

Social influence is positively associated with green buying behavior.

Another key element of TPB is the role of PBEC in influencing behavior. PBEC encompasses past experiences, anticipated barriers, and perceptions of access to the resources needed to perform a behavior [27]. This concept becomes particularly relevant when examining green behavior and sustainable purchasing, as consumers often express positive attitudes toward green products, yet their actual purchasing behavior may not align with these intentions [16,21]. Factors related to PBEC, such as price sensitivity and limited availability of eco-friendly options, significantly impact purchasing decisions [46]. Essentially, PBEC provides insight into the intention-action gap in sustainable consumption by explaining why strong intentions toward sustainability do not consistently translate into action. In sustainable consumption literature, the importance of PBEC is well-documented. Numerous studies highlight its influence on consumers’ intentions to purchase green, eco-friendly products [15,33,35,42,43]. This body of evidence suggests that when consumers perceive greater control over their ability to purchase green products, including factors such as affordability, accessibility, and knowledge, they are more likely to engage in green buying behavior. Thus, we propose the following hypothesis:

Hypothesis 2:

Perceived behavioral control is positively associated with green buying behavior.

As aforementioned, while the TPB emphasizes the role of attitudes in shaping behavior, substantial evidence indicates that positive attitudes toward sustainable practices do not consistently translate into actual purchasing decisions [16,18]. In fact, the association between green attitudes and sustainable behavior often appears to be weak [47,48]. Given that sustainable buying behavior generally yields greater societal benefits than personal advantages, prosocial attitudes (PROS) emerge as a particularly valuable construct for understanding consumers’ motivations in sustainable consumption contexts. This focus on prosocial attitudes reflects individuals’ broader concerns for others and collective well-being beyond self-interest.

Prosocial attitudes refer to voluntary behaviors intended to benefit others, encompassing empathy and genuine concern for others’ welfare [18,49,50,51]. Steele et al. [52] suggest that individuals with strong prosocial inclinations typically exhibit altruistic behaviors, indicative of a prosocial personality orientation. Within sustainability research, prosocial attitudes are frequently examined through the conceptual lens of altruism, which has shown a consistent positive association with various pro-environmental behaviors [53,54,55].

These prosocial attitudes can guide consumers toward choices that reflect heightened social responsibility and environmental stewardship [56,57]. For instance, Shiel et al. [58] demonstrate that individuals with stronger prosocial attitudes typically express greater concern for future generations’ well-being. This forward-looking temporal perspective fosters a heightened awareness regarding environmental preservation, motivating these individuals to adopt sustainable purchasing behaviors, driven primarily by a sense of social responsibility rather than considerations of personal convenience or immediate cost [50,59].

Additionally, prosocial attitudes have been found to play a significant moderating role in green consumer behavior, effectively bridging the gap between environmental concerns and green purchasing decisions [60,61]. This mediating function suggests that prosocial orientations may serve as a crucial psychological mechanism through which environmental awareness translates into concrete behavioral outcomes. Consequently, drawing from this substantial body of research, we consider prosocial attitudes as a key precursor to green buying and hypothesize:

Hypothesis 3:

General prosocial attitudes are positively associated with green buying behavior.

2.2. Green Consumption Values

Green consumption values (GRCV) represent a key aspect of sustainable consumer behavior, reflecting individuals’ inclination to prioritize environmental protection through their purchasing and consumption decisions [62]. These values, grounded in personal commitments to environmental stewardship, have emerged in the literature as key drivers of green buying behavior [18,63,64,65]. Consumers with strong GRCV consistently demonstrate a greater propensity to make environmentally responsible purchases and prioritize ecological considerations in their decision-making processes [18]. This relationship between GRCV and sustainable behavior has been observed across various contexts, such as organic clothing [63] and sustainable food choices [66] to general green products [62,64]. Based on this substantive body of evidence, we hypothesize that

Hypothesis 4:

Green consumption values are positively associated with green buying behavior.

2.3. Role of Demographics as Moderators

Numerous studies have investigated and frequently demonstrated that demographic characteristics moderate the relationships between sustainable behavior and its antecedents [67,68,69,70,71]. Research indicates that age exerts both direct and indirect effects on sustainable consumption patterns. While several studies suggest that older consumers exhibit stronger green buying intentions [69,72], others report that younger consumers demonstrate more sustainable behavior in certain contexts [35]. Gender has similarly been identified as a significant moderator of sustainability outcomes. Female consumers are consistently associated with stronger attitudes and subjective norms regarding environmental actions; however, findings remain mixed, with both males and females demonstrating higher propensities toward sustainable purchasing depending on product category and consumption context [35,69,72]. Socioeconomic factors also play important roles—higher-income consumers generally display greater engagement with sustainable behavior, while higher educational attainment robustly influences TPB components and correlates with increased sustainable consumption practices [69,73]. Despite these observed patterns, no clear consensus has emerged regarding the precise mechanisms through which these demographic variables moderate sustainable consumption. The moderating effects of age, gender, and income vary considerably across contexts, with researchers reporting both insignificant effects and contradictory findings. For instance, some studies indicate that middle-income rather than high-income and younger consumers rather than older demonstrate more sustainable behavior [35,68,70,72,73]. To contribute to this ongoing scholarly dialogue and provide further clarity regarding potential demographic moderations, we propose the following:

Hypothesis 5:

Demographics act as a moderator between green buying behavior and its antecedents.

2.4. Research Model

Figure 1 presents the conceptual research model developed upon the proposed hypotheses and the theoretical foundations.

3. Method

3.1. Sampling and Data Collection

The study targeted adults in the UK, employing convenience sampling to reach this population. Data for this cross-sectional investigation were collected through an online questionnaire deployed on the Qualtrics platform. All scales utilized reflective items measured on 7-point Likert scales (1 = Strongly Disagree to 7 = Strongly Agree). Prior to full deployment, the questionnaire underwent pretesting and subsequent refinement based on feedback from four academic peers and twenty-five university students. The scales and their corresponding items were adapted from established studies, with detailed descriptions presented in Appendix A.

The final questionnaire was distributed through online discussion groups on LinkedIn, Facebook, and Twitter over a four-week period from June to July 2022. Potential respondents were informed about the study’s purpose and provided informed consent via the survey’s welcome page before participation. From the initial 455 responses received, 436 were retained for analysis after filtering out incomplete and low-quality submissions. Specifically, data quality screening included checking for straight-lining patterns (respondents selecting identical answers across all questions) to identify inattentive participants. Consequently, 8 incomplete and 11 low-quality responses were excluded listwise to preserve the integrity of the multivariate analysis. Prior to analysis, missing data patterns were assessed using Little’s MCAR test, which confirmed that missingness was random rather than systematic. Respondents’ demographic characteristics are provided in

Table 1. Respondent Demographics.

Measure	Item	Count	Percentage
Gender	Male	212	48.9%
	Female	219	50.2%
	Non-binary/Prefer not to say	5	0.2%
Age	18–24	78	17.5%
	25–34	154	35.5%
	35–44	119	26.7%
	45–54	56	12.8%
	55+	29	7.2%
Education	High School graduate	75	17.1%
	Undergraduate (Bachelor’s) degree	242	55.5%
	Postgraduate (Masters+) degree	114	26.0%
	Prefer not to say	5	1.0%
Annual Income	Less than £25,000	172	40.0%
	£25,000–40,000	125	28.8%
	Above £40,001	105	23.6%
	Prefer not to say	34	7.6%
Total		436	100%

.

To mitigate potential biases and common method variance (CMV) concerns typically associated with online surveys and convenience sampling, several methodological safeguards were implemented. First, the questionnaire design incorporated clear instructions and neutral language to minimize response bias. Second, the use of pretesting with both academic peers and students helped identify and rectify ambiguous or leading items, enhancing the instrument’s clarity and validity. Third, explicit statements regarding voluntary participation and anonymity were provided to reduce social desirability bias and encourage honest responses, thereby mitigating CMV. Fourth, data collection across multiple platforms (i.e., LinkedIn, Facebook, and Twitter) ensured a broader reach and increased diversity among participants, helping to counteract potential sampling bias. Fifth, constructs within the survey were intentionally separated into distinct sections to further mitigate CMV. Finally, all responses underwent rigorous screening for completeness and consistency, with any inattentive or patterned responses removed prior to analysis, thereby enhancing overall data quality.

A prior publication used part of the same dataset but addressed a distinct research objective with different variables and methodological techniques [74]. While the earlier study focused on the eco-fashion behavior and status consumption relationship and investigated customer segments, the current research investigates green consumption behavior in general. Also, the nonlinear influence of antecedents within the TPB framework and a combined use of PLS-SEM and NCA as the analysis approach are novel to this study. These distinctions in conceptual framework and methodology ensure that the present contribution is both original and theoretically novel.

3.2. Data Analysis and Results

Partial Least Squares Structural Equation Modeling (PLS-SEM) followed by Necessary Conditions Analysis (NCA), as visualized in Figure 2, is used to address the research questions and test for the hypotheses developed in the Conceptual Framework section.

3.2.1. PLS-SEM Analysis

Unlike covariance-based SEM, PLS-SEM is component-based and focuses on maximizing the explained variance of the dependent variables. The PLS-SEM process involves two sub-models: the measurement model and the structural model. The measurement model represents the relationships between observed data and latent variables, while the structural model depicts the relationships between latent variables. An iterative algorithm is used to estimate the latent variables by alternating between the measurement and structural models until convergence is achieved. This approach allows researchers to handle complex models and offers a high degree of statistical power compared to CB-SEM, showing a higher robustness. This method also allows for non-normal data distributions and is appropriate when the research aims to predict key target constructs [75,76]. PLS-SEM has gained widespread interest in managerial sciences and marketing in the last decade due to both methodological advancements and increasing scholarly support [75,76]. To carry out PLS-SEM analysis, SmartPLS 4 software was utilized on the valid data collected from 436 respondents. Path significance levels were estimated through bootstrapping with 10,000 resamples. Detailed guidelines for each stage of the analysis, including the measurement model construction and assessment, can be found in Sarstedt et al. [77].

Each item, adapted from the validated scales (outlined in Appendix A), was assigned to its corresponding construct (e.g., items measuring green consumption values were loaded onto the GRCV construct), and the research model was defined. The validity and reliability of the measurement model were assessed using established criteria and are summarized in

Table 2. Reliability and Validity Analysis Results.

	CA	CR	AVE	GRCV	GRBB	PBEC	PROS	SUBN
GRCV	0.937	0.939	0.760	0.872	0.875	0.360	0.462	0.557
GRBB	0.903	0.907	0.722	0.807	0.850	0.399	0.405	0.642
PBEC	0.756	0.877	0.665	0.322	0.361	0.816	0.270	0.419
PROS	0.945	0.949	0.786	0.436	0.378	0.207	0.886	0.312
SUBN	0.929	0.929	0.875	0.523	0.590	0.399	0.297	0.936

The HTMT values are presented in the upper right of the matrix, the square roots of AVE are located along the diagonal in bold, and the correlations between constructs are in the lower left of the matrix. GRCV: Green consumption values; PBEC: perceived behavioral control; PROS: Prosocial attitudes; GRBB: Green buying behavior; SUBN: Subjective Norms.

. Discriminant validity was verified by comparing each construct’s correlation with other constructs to the square root of the average variance extracted (AVE) values, following Fornell and Larcker’s [78] criterion. The square root of each construct’s AVE exceeded its correlations with other constructs, and inter-item correlations indicated that correlations between items measuring different latent variables remained below the 0.60 threshold. Additionally, all indicators loaded more strongly on their associated constructs than on any other constructs, further supporting discriminant validity and indicating the absence of multicollinearity issues [75,79]. As suggested in the literature, heterotrait-monotrait ratio (HTMT) values were also evaluated and found to be below the recommended 0.9 threshold [75,79], reinforcing the evidence for discriminant validity.

Convergent validity was established by evaluating Cronbach’s alpha (CA), composite reliability (CR), and average variance extracted (AVE). All CA and CR values surpassed the acceptable threshold of 0.7, and all AVE values exceeded the minimum acceptable value of 0.5. These results, displayed in Table 2, confirm that the model demonstrates satisfactory levels of reliability, convergent validity, and discriminant validity [75].

To mitigate the potential issue of common method variance, several precautions were taken during the study’s design and implementation. The questionnaire used clear and straightforward language, and respondents were assured of anonymity. Additionally, participants were explicitly informed that there were no correct or incorrect answers. During analysis, Harman’s single-factor test was conducted to evaluate the extent of common method variance, yielding a variance of 32.3%. This result, well below the commonly accepted threshold of 50%, indicates that common method variance is not a significant concern in this study [80].

The model’s goodness-of-fit was evaluated using several measures recommended in the PLS-SEM literature [79]. The standardized root mean square residual (SRMR) value was 0.052, indicating a good fit [81]. The coefficient of determination (R²) for the latent variable GRBB was 0.693, suggesting that the model accounted for a substantial amount of variance in the green buying behavior variable [79].

The research also employed PLSpredict, a recommended approach for evaluating PLS-SEM model performance. This method assesses the predictive accuracy of the structural model by comparing it with predictions generated by linear regression models (LM) for the manifest variables. PLSpredict runs linear regressions of each dependent construct indicator on the indicators of the latent variables in the research model [82,83]. Following methodological guidelines, a k-fold cross-validation procedure was implemented with ten folds (k = 10), systematically dividing the sample into subgroups [82].

Table 3. PLSPredict Predictive Model Assessment Results.

	PLS-SEM		LM	PLS-SEM–LM *
	Q2_predict	RMSE	RMSE	RMSE
GRBB1	0.383	1.272	1.283	−0.011
GRBB2	0.462	1.175	1.204	−0.029
GRBB3	0.549	1.231	1.269	−0.038
GRBB4	0.488	1.189	1.232	−0.043
GRBB5	0.570	1.056	1.097	−0.042

* When all indicators in the PLS-SEM analysis yield smaller prediction errors compared to the linear regression model (PLS-SEM < LM), this indicates high predictive power.

presents the comparative analysis results, showing the prediction errors of the PLS-SEM model and the errors of the linear regression model (LM). Since the errors displayed a roughly symmetrical distribution, RMSE values were assessed [83]. The results indicate that, for all indicators, the PLS-SEM model exhibited smaller prediction errors than the LM model. This provides strong evidence of the model’s high predictive power [82]. Given these findings, the model was deemed to fit the data adequately and demonstrated high predictive power. Moreover, we checked variance inflation factors (VIFs) for all constructs, and all were below 5, indicating that multicollinearity is not a concern.

3.2.2. Necessary Condition Analysis (NCA)

NCA complements traditional methods like regression and structural equation modeling by assessing necessary conditions that vary in type and degree [19,20]. NCA offers two key contributions: first, it identifies ceiling lines and bottleneck tables to visualize and interpret predictor-outcome relationships; second, it calculates parameters such as ceiling line accuracy, effect sizes, and significance tests to ensure analytical precision [19,84]. Using a Cartesian plot, NCA maps predictor values (X-axis) against outcomes (Y-axis), drawing a ceiling line between observed and empty zones. There are several techniques for defining the ceiling line. The most robust is suggested to be ceiling envelopment with free disposal hull (CE-FDH), which creates a stepwise function along the upper-left observations. The size of the empty upper-left space reflects the necessity of the predictor for the outcome, summarized in bottleneck tables that quantify how predictor X constrains outcome Y [19].

4. Findings

4.1. Path Analysis Results

Table 4. Path Analysis and Hypothesis Testing.

Paths	Hypothesis	Path Coefficients	Standard Deviation	T Statistics	p Values	Effect Size f2
GRCV → GRBB	H4 Accept	0.674	0.043	15.512	0.000	0.935
PBEC → GRBB	H2 Reject	0.057	0.034	1.683	0.092	0.009
PROS → GRBB	H3 Reject	0.009	0.039	0.232	0.817	0.000
SUBN → GRBB	H1 Accept	0.212	0.047	4.514	0.000	0.097

GRCV: Green consumption values; PBEC: perceived behavioral control; PROS: Prosocial attitudes; GRBB: Green buying behavior; SUBN: Subjective Norms.

presents the path coefficients, significance levels, hypothesis testing results, and effect sizes, while Figure 3 visualizes the path coefficients alongside the R² values.

The findings confirmed that green consumption values and social influence are positively associated with green buying behavior, confirming two of the proposed hypotheses (H₁ and H₄). Conversely, no significant relationship was observed between prosocial attitudes or perceived behavioral control and green buying behavior, and H₂ and H₃ were rejected.

A lack of significant PBEC on behavior or intentions is at odds with the majority of the literature, despite similar insignificant relationships found in related studies [36,85]. The contextual nature of this relationship was discussed in the extant literature [86]. In sustainability contexts, consumers may not perceive significant barriers to engaging in green behavior, especially if eco-friendly products are widely available and accessible. Given that the sample is from the UK, consumers have access to a wide range of eco-friendly products. PBEC’s effect can vary based on the type of green behavior examined as well. For example, PBEC might be more influential for actions requiring significant effort or resource investment (e.g., installing solar panels) but less so for routine behaviors like buying eco-friendly household cleaning products.

The greatest direct impact on green buying behavior emerged from GRCV, with effect sizes indicating a very strong effect (f² = 0.935 > 0.35), followed by social influence, which has a weak effect (f² = 0.097 < 0.10) on GRBB. Green consumption values’ strong positive association with green buying behavior aligns with previous literature that suggests individuals with high GRCV prioritize sustainability in their purchasing choices, emphasizing the role of personal values in eco-friendly decision-making [18,63,64].

Social influence’s positive yet comparatively weaker effect highlights that while peer and societal pressures play a role, intrinsic motivations such as GRCV are more compelling for green consumers. This finding aligns with the idea that sustainable consumption is often driven by deeply held personal values rather than external pressures. Green consumers who prioritize environmental values may be less susceptible to social cues, as their decisions are guided more by internalized beliefs than by the need for social approval. Moreover, in contexts where green behaviors are becoming normative or expected, the novelty or urgency of social influence may diminish, further reducing its impact.

Interestingly, pro-social attitudes showed no direct impact on eco-behavior in the sample, suggesting altruistic inclinations do not directly impact green buying behavior in this study. Several factors might explain this finding. First, while pro-social attitudes reflect a general disposition towards helping others, they may not specifically address the environmental concerns linked to green purchasing. Without a clear connection between altruism and the perceived impact of green products, consumers may not feel compelled to act. Moreover, the disconnect could stem from the “attitude-behavior gap” often observed in sustainable consumer behavior. While individuals may express altruistic attitudes, external barriers such as cost, convenience, or lack of trust in green product claims might prevent them from converting these attitudes into action. Additionally, altruistic motivations might be overshadowed by more self-focused values like personal health, financial savings, or product quality when making purchasing decisions.

4.2. NCA Results

To get further insights into green buying behavior, NCA analysis results were assessed.

According to the necessary conditions analysis results provided in

Table 5. NCA Effect Sizes.

(CE_FDH) Y: GRBB	Effect Size (d)	Significance (p-Value)
GRCV	0.22	0.000
PBEC	0.10	0.004
PROS	0.06	0.337
SUBN	0.01	0.311

CE_FDH: Ceiling Envelopment—Free Disposal Hull. GRCV: Green consumption values; PBEC: perceived behavioral control; PROS: Prosocial attitudes; GRBB: Green buying behavior; SUBN: Subjective Norms.

, green consumption values and perceived behavioral control are significant necessary conditions for green buying behavior.

The effect sizes indicate that perceived behavioral control has a weak size effect (d = 0.10), whereas green consumption values have a medium size effect (0.10 < d < 0.30) as necessary conditions [19]. Each necessary condition is assessed in detail using bottleneck tables provided in

Table 6. NCA Bottleneck Table.

Y:GRBB	CE-FDH
	GRCV	PBEC	PROS	SUBN
0	NN	NN	NN	NN
10	NN	NN	NN	NN
20	10.8	NN	NN	NN
30	16.6	NN	NN	NN
40	21.7	NN	NN	NN
50	21.7	NN	2.7	NN
60	24.9	NN	2.7	NN
70	24.9	NN	2.7	NN
80	25.1	20.2	2.7	NN
90	50.8	45.3	5.4	NN
100	52.8	66	69.3	16.7

NN: not necessary; GRCV: Green consumption values; PBEC: perceived behavioral control; PROS: Prosocial attitudes; GRBB: Green buying behavior; SUBN: Subjective Norms.

. According to the results, in order to reach a 50% level of green buying behavior, two necessary conditions must be met: Green consumption values at 21.7% and prosocial attitudes at 2.7%. On the other hand, to reach a high level of green buying behavior (80%+), three necessary conditions must be met: Green consumption values at no less than 25.1%, perceived behavioral control at no less than 20.2%, and prosocial attitudes at no less than 2.7%.

The necessary conditions analysis further underscored the importance of GRCV and also demonstrated PBEC as a foundational requirement for achieving green buying behavior. Interestingly, PBEC showed a weak effect size as a necessary condition, while GRCV remained crucial across all analyzed levels of green buying behavior. This suggests that consumers may need an underlying belief in their capability (i.e., PBEC) to engage in green behavior, even if PBEC does not directly trigger action without sufficient GRCV.

4.3. Moderation Analysis Results

A moderation analysis using four demographic variables (i.e., gender, age, education, and income) was carried out to test for H₅ on SmartPLS 4. The age variable was categorized into two groups, considering the different phases of the adult life span: young adults (aged 18–34) and middle-aged and older adults (aged 35+) [87]. Gender was analyzed in two groups, and income and education in three groups each, as shown in Table 1. Grouping age ranges helped ensure adequate sample sizes within each category for meaningful comparative analysis while maintaining analytical simplicity and statistical robustness.

The results revealed two significant moderation effects: age moderated the relationships between green consumption values and green buying behavior (Age × GRCV → GRBB, t-stat: 2.306) and between prosocial attitudes (PROS) and GRBB (Age × PROS → GRBB, t-stat: 2.362). To further explore these interactions, a multigroup analysis was performed, and the findings are detailed in

Table 7. Significant Multigroup Analysis Results.

Paths	Young Mean	Old Mean	St. Dev Young	St. Dev Old	t-Value Younger	t-Value Older	Difference Young-Old	Difference p-Value
GRCV → GRBB	0.576	0.777	0.067	0.055	8.591 ***	14.151 ***	−0.202 *	0.017
PROS → GRBB	0.123	−0.086	0.059	0.067	2.091 *	1.292	0.209 *	0.021

GRCV: Green consumption values; PROS: Prosocial attitudes; GRBB: Green buying behavior * p < 0.05; *** p < 0.001.

. No significant moderation effects were observed for education, income, or gender. This finding may be attributed in part to the relative homogeneity of the sample with respect to higher educational and moderate to higher income levels. The lack of variability in these variables could reduce the statistical power needed to detect moderation effects. From a theoretical standpoint, the absence of moderation effects may also reflect evolving dynamics in sustainability-oriented consumption. Prior studies often report gender and education as significant moderators, with women and more educated individuals typically showing stronger environmental concern [71]. However, recent research suggests that green consumption values are becoming more culturally normalized and less dependent on socio-demographic distinctions [23]. This convergence may diminish the moderating influence of traditionally significant demographic traits. Moreover, the strong direct effect of GCV on green buying behavior (β = 0.674, p < 0.001, f² = 0.935) likely overwhelmed potential interaction effects with gender, income, or education. In other words, personal values, rather than background characteristics, appear to be the dominant driver of eco-conscious purchasing in this sample.

The effect of green consumption values on green buying behavior is greater in the older subsample. This difference can be attributed to several factors. Older individuals often possess greater life experience, financial independence, and stability, which enable them to better align their values with their purchasing decisions and afford the premium costs often associated with sustainable options. In contrast, younger generations may face financial constraints or competing priorities, contributing to the significant attitude-behavior gap observed among this demographic [4,6].

Interestingly, differing from the total sample and the older subsample, prosocial attitudes emerged as significant predictors of green buying behavior in the younger age group. This suggests that younger individuals are more driven by altruistic motivations, potentially because they adopt a more forward-looking perspective. With a longer time horizon ahead, younger consumers may feel a greater sense of personal investment in sustainable practices, viewing their behavior as a means of safeguarding the future for themselves and subsequent generations. This finding echoes studies that highlight generational differences in sustainability priorities, with younger individuals placing greater emphasis on global and collective well-being [88,89].

4.4. Robustness Check: Non-Linear Model

In addition to the original path model, we added quadratic effects to the proposed relationships between green buying behavior and its antecedents to consider potential non-linear relationships. Given that non-linear relationships are often overlooked yet are demonstrated to be present in various consumer behavior studies [90,91,92], this emerges as a promising way of both checking robustness and arriving at deeper insights into complicated consumer behavior. To test this model, we added a quadratic interaction term for each relationship and adopted the two-stage approach for model estimation in SmartPLS 4 [93].

Table 8. Nonlinear model results.

Paths	Path Coeff.	Standard Deviation	T Statistics	p Values
GRCV → GRBB	0.567	0.050	11.416	0.000
PBEC → GRBB	0.080	0.044	1.809	0.071
PROS → GRBB	0.062	0.044	1.417	0.156
SUBN → GRBB	0.233	0.044	5.341	0.000
QE * (GRCV) → GRBB	−0.101	0.026	3.828	0.000
QE (PROS) → GRBB	0.037	0.026	1.465	0.143
QE (SUBN) → GRBB	0.049	0.031	1.563	0.118
QE (PBEC) → GRBB	−0.010	0.022	0.472	0.637

* QE: Quadratic effect term to account for non-linear relationships. GRCV: Green consumption values; PBEC: perceived behavioral control; PROS: Prosocial attitudes; GRBB: Green buying behavior; SUBN: Subjective Norms.

presents the results of the model in which quadratic effects are considered. The only significant quadratic relationship was between green consumption values and green buying behavior, with an effect size of 0.040. The other nonlinear effects were found to be insignificant. According to the suggestions in the literature, this indicates a medium effect size [94,95]. The relationship between green consumption values and green buying behavior is plotted to include both the linear and the non-linear elements to offer a more convenient way to assess it. The coefficients indicate a logarithmic relationship between GRCV and green buying behavior, as visualized in Figure 4. This non-linear effect suggests that while GRCV strongly motivates green purchases initially, its incremental impact lessens as values intensify.

5. Discussion

The findings of this study are discussed under two headings in this section: first, through a theoretical lens to highlight the theoretical implications; and second, from a managerial perspective to provide actionable, practical insights.

5.1. Theoretical Implications

This study offers several theoretical implications that challenge and expand existing consumer behavior frameworks, particularly the Theory of Planned Behavior (TPB), in sustainability contexts. This study extends the TPB framework by integrating Green Consumption Values (GRCV) as a value-based antecedent. GRCV functions as a moral and identity-related driver that can influence behavioral intention, complementing the attitudinal and normative pathways already established within TPB. While this study focuses on the UK, its findings may have broader relevance to other countries with similar socioeconomic and cultural characteristics. As a developed economy, the UK shares key attributes with other Western nations, such as high levels of environmental awareness, widespread access to sustainable products, and a growing emphasis on ethical consumption.

First, the lack of a significant relationship between perceived behavioral control and green buying behavior questions the universal applicability of TPB in explaining eco-friendly consumption. While PBEC is typically considered a key driver in TPB, its non-significance here echoes findings of certain studies [36,85] and suggests that consumers’ perceptions of control may be less relevant when strong intrinsic motivations, such as green consumption values, are present. Alternatively, PBEC may be more relevant in high-cost or effort-intensive consumption contexts.

Second, the strong positive relationship between green consumption values and green buying behavior reinforces the significance of personal values in driving eco-friendly choices. This aligns with existing literature suggesting that individuals with high GRCV are more likely to prioritize sustainability in their consumption decisions [18,63,64]. The substantial effect size (f² = 0.935) emphasizes that intrinsic values far outweigh other determinants, positioning GRCV as a cornerstone in theoretical models explaining green behavior. These insights stress the need to integrate similar individual factors into existing consumer behavior models (e.g., TPB) for promoting sustainable product adoption.

Third, the weaker yet positive influence of social factors highlights that while peer and societal pressures contribute to green purchasing behavior, their impact is relatively limited compared to intrinsic motivations (i.e., GRCV). This distinction underscores the need for theoretical frameworks to differentiate between internalized value-driven behavior and externally motivated actions, particularly in contexts of sustainable consumption.

Fourth, the non-significant impact of prosocial attitudes on GBB among the total sample deviates from the extant literature, which often links altruistic motivations with green purchasing. This finding suggests that while prosocial attitudes may reflect a general concern for others, they do not necessarily translate into actionable, eco-friendly behavior. However, the moderating role of age in this relationship, with significant effects observed among younger respondents, invites further exploration of contextual and demographic factors. This finding points to the importance of considering how generational differences and other mediators, such as environmental knowledge or lifestyle, might shape the relationship between prosocial attitudes and green buying behavior.

Fifth, Necessary Condition Analysis (NCA) revealed that both GRCV and PBEC are essential for achieving higher levels of green buying behavior, even if PBEC lacks direct predictive power. The weak effect size of PBEC as a necessary condition (d = 0.10) suggests that consumers require a baseline level of perceived capability to act on their green values. This finding aligns with the logic of non-compensatory causality, where certain enablers (like perceived behavioral control) must be present to allow behavior, even if they do not drive it directly. In this case, individuals may need to feel capable of acting (e.g., having access to sustainable products or feeling confident in their ability to assess green claims), but once that threshold is met, further increases in perceived control do not enhance the likelihood of acting green. This supports the interpretation that PBEC functions more as a “gatekeeper” rather than a “motivator” in the green purchasing process. PBEC acts as a boundary condition that constrains action only when absent. This finding highlights the importance of integrating necessity-based analyses into sustainability research, offering a complementary perspective to conventional path models.

Sixth, the identified logarithmic relationship between GRCV and green buying behavior supports emerging interest in non-linear models within consumer behavior research [90,91,92,96]. Initially, individuals with low to moderate green values show steep increases in green buying behavior as their values strengthen. However, beyond a certain point, further intensification of GRCV yields diminishing behavioral gains. At high GRCV levels, intrinsic satisfaction from “doing enough” may attenuate the perceived urgency to escalate green purchases, a phenomenon consistent with cognitive dissonance theory triggered by value saturation and motivational plateaus [91]. Once consumers internalize sustainability as a core personal value and adopt key green behaviors, additional value reinforcement may no longer significantly alter their consumption patterns [97]. This pattern resembles satisficing behavior in decision theory [98], wherein individuals feel they have ’done enough’ once a certain level of responsible behavior is achieved. The observed behavioral plateau aligns with the concept of moral saturation, where incremental increases in moral or ethical concern fail to produce proportional behavioral changes [96]. This finding warrants further exploration of the diminishing returns phenomenon in value-driven behaviors, particularly in sustainability studies.

Finally, the findings highlight the limited moderating role of demographics. The absence of significant moderation effects by gender, education, and income suggests that green buying behavior and its antecedents are consistent across diverse consumer groups but differ between generations. This finding challenges the traditional focus on demographics in segmentation strategies, shifting theoretical attention towards psychographic and behavioral variables as stronger predictors.

5.2. Managerial and Practical Implications

For practitioners, the results underscore the critical role of fostering green consumption values among consumers. Marketing strategies should thus focus on value-driven messaging that resonates with consumers’ existing environmental values while seeking to cultivate such values in broader audiences. Educational campaigns, green certifications, and transparency in sustainable practices can help reinforce GRCV as a driver of green purchasing.

The diminishing return effect of GRCV and the NCA results suggest that efforts to promote green products should prioritize consumers with moderate levels of GRCV, as they are most likely to respond to interventions. Tailored campaigns that resonate with this group (e.g., emphasizing product authenticity, highlighting environmental impact, or leveraging relatable green role models) could maximize behavior change without expending excessive resources on individuals with already high GRCV. Marketing efforts should diversify messaging at higher levels of GRCV. For highly committed green consumers, brands may need to highlight other value-added aspects (e.g., product quality or community impact) that enhance the appeal of eco-friendly products beyond mere green credentials.

The age-related differences in green buying behavior offer valuable insights for tailoring marketing campaigns. For older consumers, campaigns emphasize the alignment of eco-friendly products with deeply held personal values, underscoring themes of consistency, responsibility, and legacy. Conversely, campaigns targeting younger audiences should focus on their altruistic tendencies and collective aspirations. Messaging should underscore how their individual choices contribute to global sustainability, appealing to their sense of purpose and shared responsibility for creating a better future.

The findings also indicate a strategic benefit in leveraging social influence as a supporting element. While not as strong as GRCV, social influence can act as a complementary factor, suggesting that companies might benefit from social proof tactics, such as influencer partnerships or community-based initiatives, to reinforce green buying behavior.

Finally, the findings indicate that perceived behavioral control is a necessary but not sufficient condition for green buying behavior, suggesting that consumers must feel empowered to act sustainably, but this alone does not guarantee action. To design effective interventions, it is critical to enhance PBEC in a way that complements rather than undermines intrinsic motivations like green consumption values. Communications may highlight how individual actions contribute to broader environmental goals, reinforcing a sense of agency. Providing diverse options for sustainable products may allow consumers to make eco-friendly decisions aligned with their personal preferences, preserving autonomy so that sustainable consumption feels like an active, value-driven choice rather than an obligation.

5.3. Limitations and Future Scope

While this study offers valuable insights, it is important to acknowledge its limitations, primarily rooted in its methodology. This study employed a convenience sampling method, primarily distributing the survey through social media platforms (i.e., LinkedIn, Facebook, and Twitter). As a cross-sectional survey reliant on self-reported perceptions, there’s potential for a gap between stated intentions and actual consumer behavior. Inherent to the methodology, biases such as social conformity and acquiescence may influence findings as well. Furthermore, the non-random sampling method employed (i.e., convenience sampling), though common in cross-sectional research, may limit the generalizability of the findings. The use of several social media platforms to collect data facilitated access to a broad audience, yet it may have introduced sampling bias, favoring a younger, more digitally active respondent profile. Future studies employing larger, more diverse samples obtained through stratified sampling and using expanded recruitment channels will reduce such platform-based biases and demographic skewness. Also, this methodology does not account for considering changing perceptions, attitudes, or intentions over time.

The prosocial attitude construct was measured using a general scale rather than one specific to environmental behaviors. While this aligns with previous studies examining broad value orientations, a more domain-specific measure might better capture the environmental intent behind prosocial behavior. This suggests an opportunity for future research to refine construct alignment.

Our study opens up several promising avenues for future research. Incorporating behavioral data, such as tracking actual purchase behavior or using experimental designs, could provide a more objective measure of green buying behavior. Observational or experimental methods would also enable comparison between stated intentions and actual behavior. Future studies could adopt longitudinal or quasi-experimental methods such as difference-in-differences (DID) for causal inference. Moreover, the insignificant effect of PBEC and pro-social attitudes on green buying behavior presents an area to focus on. Future research could consider these non-significant relationships by exploring potential mediators (e.g., trust in green claims) or moderators (e.g., product price, product attributes, perceived effort, cultural factors). Studies that focus on a particular product group will shed light on context-specific influences. Experimental studies could also test under what conditions PBEC or pro-social attitudes become more influential.

Moreover, addressing barriers highlighted in existing literature could provide valuable insights. These include a lack of variety and availability in products, higher price points, and consumer distrust towards brands’ sustainability claims due to greenwashing [11,99,100]. Examining how these factors interact with green buying behavior may offer important insights into effective communication strategies for promoting such behavior.

Finally, future research may also consider what specific cultural or societal factors may amplify or dampen the generational differences detected in this study. Given that sustainability perceptions and green purchase behavior may differ by cultural context, comparative studies across diverse geographic regions would enhance the external validity of the model. The barriers younger consumers face in acting on GRCV, such as economic constraints or competing priorities, may be explored. Moreover, how these differences might evolve as younger cohorts age and accumulate life experiences could deepen our understanding of how age shapes sustainable consumption over time.

6. Conclusions

This study contributes to the literature on green buying behavior by expanding the TPB through the integration of green consumption values and prosocial attitudes reflecting both consumers’ environmental concerns and social-altruistic attitudes. The two-step analytical approach using PLS-SEM and NCA enabled a comprehensive examination of both sufficient and necessary conditions for green buying behavior, offering new insights into sustainable consumption.

The findings reveal that green consumption values and social influence are positively associated with green buying behavior, with green consumption values exerting the most substantial impact, yet not linearly but logarithmically. These results underscore the importance of personal values in shaping eco-friendly purchasing decisions, suggesting that fostering such values can be a powerful approach to encouraging sustainable consumption. Additionally, the study highlights that while perceived behavioral control and prosocial attitudes did not show a direct effect on green buying behavior, they play a role as necessary conditions at specific threshold levels, suggesting a more nuanced influence on green purchasing.

By addressing both the necessary and sufficient conditions, this study provides a novel approach to bridging the attitude-behavior gap in sustainable consumption. The inclusion of NCA allows for a deeper understanding of critical thresholds for green buying behavior, pinpointing levels of GRCV and PBEC required to drive meaningful engagement in eco-friendly purchasing. These insights pave the way for future research and offer actionable implications for sustainable marketing strategies, particularly for brands aiming to appeal to value-driven consumers.