Fast Carts, Slow Trust: When Sustainability Signals Backfire in Customers’ Cognitive Evaluation of Quick Commerce Platforms

1Introduction

A litre of milk at the door within ten minutes was a novelty only a few years ago; in India’s large cities it is now routine. Quick commerce (q-commerce) platforms such as Blinkit, Zepto, Swiggy Instamart and BigBasket BB Now combine neighbourhood dark stores, real-time inventory systems and app-based ordering to fulfil small, frequent orders almost instantly. The format has scaled with remarkable speed: India’s q-commerce gross merchandise value reached an estimated US$10–11 billion in 2025 after doubling each year, equivalent to roughly 16–17% of national e-retail value (Bain & Company, 2026).

The features that make q-commerce convenient also make its sustainability contestable. Every ten-minute delivery adds packaging, a motorbike trip and a share of a dark store’s footprint, and the economics of ultra-fast fulfilment remain uncertain. Platforms have responded with green delivery options, recyclable packaging, sustainability rewards and public commitments. This study conceptualises customers’ reading of such commitments as sustainable finance orientation (SFO): the extent to which customers perceive a platform as financially and strategically committed to sustainability-oriented practices. The construct follows the view of sustainable finance as the integration of environmental, social and governance considerations into business decisions (Schoenmaker & Schramade, 2019).

Whether customers reward these efforts is far from obvious. Classic models predict that whatever raises perceived value should improve evaluation (Kim et al., 2007; Zeithaml, 1988), and meta-analytic evidence identifies perceived value as a powerful predictor of customer outcomes (Blut et al., 2024). Sustainability claims, however, are difficult for customers to verify, and their persuasive power depends on whether customers trust them (Mume & Sun, 2026). In a business model whose environmental costs are visible on every doorstep, green messages may invite scepticism as readily as approval. Environmentally concerned customers may also respond differently, because environmental concern shapes how consumers weigh ecological information (Dunlap et al., 2000; Haws et al., 2014; Hudayah et al., 2023).

Research on q-commerce has so far emphasised adoption drivers, service quality, instant gratification and satisfaction (Chavadi et al., 2024; Panesar et al., 2026), whereas research on sustainability perceptions has concentrated on green products, CSR and ESG in other sectors (Hasan et al., 2024; Shih et al., 2024). How sustainability-oriented platform practices translate into customers’ reasoned judgements of q-commerce platforms remains unexamined. The study therefore asks whether SFO raises customer perceived value (CPV) and customer cognitive evaluation (CCE), whether CPV carries the effect of SFO to CCE and whether environmental concern (EC) intensifies the effect of SFO. In answering these questions, it introduces SFO as a customer-perceived construct for platform retail, documents a counter-intuitive negative value–evaluation relationship in q-commerce and clarifies that environmental concern acts as a direct rather than an amplifying influence.

2Literature review and hypothesis development

2.1Theoretical lens

The stimulus–organism–response (S-O-R) framework holds that external stimuli shape internal appraisals, which in turn drive responses; it is widely used to explain reactions to sustainability cues such as packaging and perceived sustainability (Kıymalıoğlu et al., 2024; Shih et al., 2024). In the present model, SFO is the stimulus, CPV the organismic appraisal and CCE the response. Value-based reasoning complements this lens: customers evaluate technologies and services by weighing benefits against sacrifices (Kim et al., 2007; Zeithaml, 1988), so sustainability practices should matter to the extent that they alter this trade-off.

2.2Quick commerce and customer cognitive evaluation

Q-commerce differs from conventional e-commerce in its reliance on proximity fulfilment, very short delivery windows and habitual, low-involvement shopping missions. Technological, marketing and social stimuli and service quality raise perceived value and trust, which drive q-commerce usage intention (Panesar et al., 2026); instant gratification improves engagement and satisfaction (Chavadi et al., 2024); and in online food delivery, price, payment ease and time efficiency are central to loyalty (Siddiqi et al., 2024). Customer cognitive evaluation (CCE) is defined here as the customer’s reasoned judgement that a platform is useful, suitable, beneficial and worth using. It builds on the cognitive beliefs that underpin technology acceptance, notably perceived usefulness and performance expectancy (Davis, 1989; Venkatesh et al., 2003), which, together with trust, also shape purchase intention in sustainability-oriented e-commerce (Farhat et al., 2025).

2.3Sustainable finance orientation and perceived value

Customers who believe a platform invests in responsible delivery, reduced packaging or sustainability rewards may perceive added functional, emotional and social value. CSR research shows that perceived responsibility improves customer judgements when it appears grounded in genuine practice (Pérez & Rodríguez del Bosque, 2013; Turker, 2009); ESG practices shape customer behaviour towards brands (Hasan et al., 2024); and perceived sustainability enhances emotional and social value (Shih et al., 2024). Hence:

H1: ustainable finance orientation positively influences customer perceived value.

2.4Perceived value and cognitive evaluation

Perceived value is the customer’s overall judgement of utility based on what is received relative to what is given (Zeithaml, 1988) and spans functional, emotional, social and price dimensions (Sweeney & Soutar, 2001). It is a cornerstone of technology adoption (Kim et al., 2007), predicts satisfaction, trust and loyalty on digital platforms (Yum & Kim, 2024) and drives repurchase intention in B2C e-commerce (Miao et al., 2022). In q-commerce, customers who perceive that speed, convenience, availability and promotions outweigh fees and effort should reach more favourable judgements of the platform. Hence:

H2: ustomer perceived value positively influences customer cognitive evaluation.

2.5Sustainable finance orientation and cognitive evaluation

Sustainability practices may also shape evaluation directly by signalling responsibility and long-term orientation (Pérez & Rodríguez del Bosque, 2013; Turker, 2009). Such a signal is persuasive only if customers trust it (Mume & Sun, 2026), and the visible footprint of ultra-fast delivery could weaken that trust. Following the dominant theoretical expectation, however, we propose:

H3: ustainable finance orientation positively influences customer cognitive evaluation.

2.6The mediating role of perceived value

S-O-R logic implies that stimuli affect responses chiefly through internal appraisals. Consistent with this, perceived sustainability affected satisfaction only through perceived value in the study by Shih et al. (2024), and online environmental platform services shaped green consumer behaviour through attitudinal and evaluative mediators in the study by Rana et al. (2024). SFO may therefore shape CCE by first altering CPV:

H4: ustomer perceived value mediates the relationship between sustainable finance orientation and customer cognitive evaluation.

2.7The moderating role of environmental concern

Environmental concern reflects awareness of environmental problems and willingness to act on them in consumption (Dunlap et al., 2000; Haws et al., 2014). Concerned customers should notice and weigh sustainability practices more heavily than customers focused on price and speed. Environmental concern moderated the effect of green perceived value on purchase intention among Generation Z consumers (Hudayah et al., 2023), and digital platforms can shape green attitudes by supplying information (Rana et al., 2024). Figure 1 presents the resulting model.

H5: nvironmental concern moderates the relationship between sustainable finance orientation and customer cognitive evaluation, such that the relationship is stronger at higher levels of environmental concern.

Note. Solid arrows show hypothesised direct effects; the dashed arrow shows the hypothesised moderating effect. Authors’ own conceptualisation.

3Research method

3.1Context, sampling and data collection

Bengaluru was chosen as the research setting because of its intensive use of app-based retail, digital payments and rapid delivery services. The population comprised residents who had bought groceries, food, household or other daily-use products through at least one q-commerce platform, and a screening question confirmed this experience. Because no sampling frame of q-commerce users exists, convenience sampling was used, an approach common in theory-testing consumer research although limited in generalisability. A structured questionnaire was administered on a voluntary basis, with assurances of anonymity, confidentiality and purely academic use. The final sample comprised 390 valid responses. The inverse square root method (Kock & Hadaya, 2018) indicates that this sample detects path coefficients of about |0.13| or larger with 80% power at the 5% significance level, meeting PLS-SEM guidelines (Hair et al., 2022); smaller coefficients are therefore interpreted with caution.

3.2Measurement instrument

All constructs were operationalised reflectively with items adapted from validated scales and reworded for q-commerce (Table 1). SFO drew on CSR image, CSR perception and sustainability-practice scales (Pérez & Rodríguez del Bosque, 2013; Turker, 2009); CPV on perceived value, PERVAL and value-based adoption research (Kim et al., 2007; Sweeney & Soutar, 2001; Zeithaml, 1988); CCE on perceived usefulness, performance expectancy and technology-evaluation scales (Davis, 1989; Venkatesh et al., 2003); and EC on the New Ecological Paradigm and green consumption values scales (Dunlap et al., 2000; Haws et al., 2014). Responses were recorded on a five-point Likert scale (1 = strongly disagree, 5 = strongly agree). A pretest checked wording and contextual fit and led to minor refinements.

Construct	Item	Statement	Loading
Sustainable finance orientation (SFO)	SFO1	This quick commerce platform supports environmentally responsible business practices.	0.964
	SFO2	This platform encourages sustainable choices through discounts, rewards or financial incentives.	0.974
	SFO3	This platform invests in sustainable delivery, packaging or operational practices.	0.973
	SFO4	This platform communicates its sustainability-related initiatives clearly to customers.	0.963
	SFO5	This platform balances business growth with environmental and social responsibility.	0.971
Customer perceived value (CPV)	CPV1	Using this quick commerce platform provides good value for the money I spend.	0.968
	CPV2	The benefits I receive from this platform are greater than the cost and effort involved.	0.971
	CPV3	This platform saves my time and improves my shopping efficiency.	0.968
	CPV4	Overall, this platform is worthwhile compared with other shopping alternatives.	0.967
	CPV5	The sustainability-related initiatives of this platform increase its overall value for me.	0.967
Customer cognitive evaluation (CCE)	CCE1	I consider this quick commerce platform a useful option for my shopping needs.	0.966
	CCE2	I believe this platform is a smart choice for quick and convenient purchases.	0.965
	CCE3	I think this platform provides a rational and beneficial shopping solution.	0.965
	CCE4	Compared with other shopping alternatives, this platform appears more suitable for my needs.	0.970
	CCE5	I evaluate this platform positively because of its service quality and sustainability orientation.	0.963
	CCE6	Overall, my judgement of this quick commerce platform is favourable.	0.963
Environmental concern (EC)	EC1	Environmental protection is important to me when making consumption decisions.	0.964
	EC2	I consider environmental impact when choosing shopping platforms.	0.970
	EC3	My purchasing decisions are influenced by concern for the environment.	0.978
	EC4	I prefer platforms that try to reduce environmental harm.	0.966
	EC5	I am willing to support responsible platforms even when it requires some extra effort.	0.964
	EC6	Environmental protection dominates my consumption decisions.	0.961
	EC7	My purchasing decisions look for the environment.	0.968

Note. N = 390. All loadings significant at p < .001 (bootstrapping). Source scales are listed in Section 3.2.

3.3Analytical strategy and method bias

PLS-SEM suited the study’s prediction-oriented aims and its combination of latent constructs, mediation and moderation, and it is well established in marketing and digital platform research (Hair et al., 2021, 2022; Sarstedt et al., 2022). Estimation used SmartPLS 4 (Ringle et al., 2024) in two stages. The measurement model was assessed through outer loadings, Cronbach’s alpha, composite reliability (ρA, ρC), average variance extracted (AVE) and the heterotrait–monotrait ratio (HTMT; Henseler et al., 2015). The structural model was assessed through bootstrapped path coefficients, R² and the standardised root mean square residual (SRMR). The indirect effect was classified following Zhao et al. (2010), and moderation was tested with an SFO × EC interaction term. Because all data came from one self-administered questionnaire, procedural remedies against common method bias were applied, including guaranteed anonymity and simple, neutral item wording (Podsakoff et al., 2003). The low inter-construct HTMT values (maximum 0.422) further suggest that a single method factor did not dominate the data.

4Data analysis and results

4.1Measurement model

Outer loadings ranged from 0.961 to 0.978, comfortably above the 0.708 benchmark (Hair et al., 2022). Cronbach’s alpha (0.983–0.989), ρA (0.984–1.000) and ρC (0.987–0.990) indicated strong internal consistency, and AVE values of 0.932–0.939 established convergent validity (Table 2). Because reliability above 0.95 can indicate redundant items (Hair et al., 2022), and EC reached a ρA of 1.000, the instrument could be shortened in future work. Every HTMT ratio was below 0.85, with the highest between CPV and CCE (0.422), confirming discriminant validity (Henseler et al., 2015).

Construct	α	ρA	ρC	AVE	CCE	CPV	EC	SFO
CCE	0.985	0.986	0.988	0.932	—
CPV	0.983	0.984	0.987	0.937	0.422	—
EC	0.989	1.000	0.990	0.935	0.119	0.117	—
SFO	0.984	0.986	0.987	0.939	0.202	0.257	0.066	—
SFO × EC					0.048	0.004	0.063	0.115

Note. α = Cronbach’s alpha; ρA and ρC = composite reliability; AVE = average variance extracted. The CCE–SFO columns report HTMT ratios (criterion < 0.85).

4.2Structural model

Table 3 and Figure 2 summarise the bootstrapped estimates. SFO exerted a positive, significant effect on CPV (β = 0.253, t = 5.845, p < .001), supporting H1. The remaining direct hypotheses were contradicted. CPV had a strong negative effect on CCE (β = −0.411, t = 10.728, p < .001), and SFO had a smaller negative direct effect on CCE (β = −0.090, t = 2.114, p = .035); H2 and H3 are therefore not supported, both effects being significant in the opposite direction. EC was positively related to CCE (β = 0.158, t = 3.983, p < .001), whereas the SFO × EC interaction was not significant (β = −0.049, t = 1.169, p = .243), so H5 is not supported.

H	Path	β	t	p	Decision
H1	SFO → CPV	0.253	5.845	< .001	Supported
H2	CPV → CCE	−0.411	10.728	< .001	Not supported (significant, opposite sign)
H3	SFO → CCE (direct)	−0.090	2.114	.035	Not supported (significant, opposite sign)
H4	SFO → CPV → CCE (indirect)	−0.104	5.013	< .001	Partially supported (complementary, negative mediation)
H5	SFO × EC → CCE	−0.049	1.169	.243	Not supported
—	EC → CCE (direct)	0.158	3.983	< .001	Significant
—	SFO → CCE (total effect)	−0.195	4.423	< .001	Significant

Note. N = 390; bootstrapped t statistics. R²(CPV) = 0.064; R²(CCE) = 0.210; SRMR (estimated model) = 0.052.

4.3Mediation and explanatory power

The indirect effect of SFO on CCE via CPV was negative and significant (β = −0.104, t = 5.013, p < .001), reflecting a positive first-stage path and a negative second-stage path. With the direct effect also significant and of the same sign, the mediation is complementary (Zhao et al., 2010), and the total effect of SFO on CCE was negative (β = −0.195, t = 4.423, p < .001). H4 is partially supported: CPV does carry the influence of SFO, but in the opposite direction to that theorised. The model explained 21.0% of the variance in CCE (adjusted R² = 0.204) and 6.4% in CPV (adjusted R² = 0.062), modest values typical of consumer evaluations shaped by many factors. SRMR values of 0.016 (saturated) and 0.052 (estimated) were below the 0.08 cut-off (Hu & Bentler, 1999), although PLS-SEM models are judged primarily on predictive and explanatory criteria rather than global fit (Hair et al., 2022).

Note. Standardised coefficients. ∗∗∗ p < .001; ∗ p < .05; ns = not significant. Red arrows indicate significant negative paths. Indirect effect SFO → CPV → CCE: β = −0.104, p < .001; total effect: β = −0.195, p < .001. Estimates from SmartPLS 4.

5Discussion

Green initiatives do add value. The positive SFO → CPV path shows that customers recognise sustainability-oriented financial and operational practices as value-building. This accords with evidence that perceived sustainability enriches perceived value (Shih et al., 2024) and that green packaging conditions q-commerce customers’ responses to the service (Chavadi et al., 2024).

But value invites scrutiny. The negative CPV → CCE path runs against the positive value–outcome link observed across digital settings (Blut et al., 2024; Miao et al., 2022; Yum & Kim, 2024). One explanation is that customers most conscious of the value they receive (speed, discounts, time saved) are also most conscious of what comes with it: delivery and handling fees, minimum-order thresholds, price premiums, packaging waste and over-consumption. Heightened value awareness may thus activate deliberate, critical appraisal rather than an automatic favourable verdict. In line with this reading, price sensitivity can impede green purchasing despite favourable attitudes (Rana et al., 2024), and price and time efficiency weigh heavily in delivery-platform loyalty (Siddiqi et al., 2024).

A credibility gap undermines green signals. The negative direct SFO → CCE path suggests that sustainability claims breed doubt when they are not substantiated. Customers may view sustainability programmes as window dressing in a model whose riders, dark stores and packaging make its footprint conspicuous. Because the persuasiveness of sustainability claims hinges on green trust (Mume & Sun, 2026), sustainability orientation must be backed by measurable action and transparent reporting.

Concern helps, but does not amplify. Environmentally concerned customers judged platforms more favourably, consistent with green consumption research (Dunlap et al., 2000; Haws et al., 2014). However, their concern did not strengthen the effect of SFO, implying that current sustainability signals are not yet distinctive or convincing enough to shape even their judgement.

6Contributions

6.1Theoretical contributions

The study challenges the assumption that perceived value uniformly yields favourable evaluations by showing that, in ultra-fast retail, value perceptions can coexist with, and even sharpen, critical appraisal. It extends S-O-R and value-based reasoning to platform sustainability by introducing SFO as a customer-perceived construct whose influence on evaluation is transmitted, negatively, through perceived value. It also refines the boundary conditions proposed in green consumption research (Hudayah et al., 2023) by showing that environmental concern operates as a direct driver of evaluation rather than as an amplifier of sustainability signals.

6.2Practical contributions

Table 4 translates the main findings into actions for q-commerce managers, marketers and sustainability teams.

Finding	Recommended action
SFO raises perceived value	Make sustainability visible at the point of choice: green delivery slots, packaging opt-outs and loyalty points for consolidated or sustainable baskets.
SFO and CPV depress cognitive evaluation	Replace generic green claims with verifiable metrics (e.g., plastic avoided, share of electric-vehicle deliveries), ideally validated by third parties.
Value awareness triggers scrutiny	Keep fees transparent, right-size packaging and price fairly so that value perceptions do not turn into critical judgements.
EC improves evaluation but does not amplify SFO	Engage environmentally concerned customers with evidence-based communication, while making sustainability tangible and believable for all segments.

7Limitations and future directions

The convenience sample from one metropolitan city limits generalisability; probability-based and comparative samples across cities, countries, income groups and platforms would strengthen external validity. The cross-sectional design captures perceptions at a single point, so longitudinal studies could trace how evaluations evolve with repeated exposure to sustainability initiatives, and experiments could isolate the effects of specific green messages, incentives and delivery options. The modest R² values call for richer models that add platform trust, service quality, price fairness, delivery reliability, satisfaction and perceived greenwashing. Above all, the unexpected negative paths merit direct investigation: future research should test whether greenwashing perceptions, price sensitivity, trust deficits or sustainability scepticism account for them, and should replicate the CPV → CCE relationship using shorter, less redundant measures in view of the very high reliability values observed here.

8Conclusion

Fast carts do not automatically earn trust. Evidence from 390 q-commerce users in Bengaluru shows that customers credit platforms’ sustainable financial and operational practices with added value, yet this value, and sustainability orientation itself, is associated with more critical cognitive evaluation of the platform. Environmentally concerned customers judge platforms more favourably, but their concern does not magnify the effect of sustainability orientation. For q-commerce platforms, the path forward lies in moving beyond symbolic sustainability messaging towards practices that are credible, measurable and demonstrably beneficial to customers.

References

1. [1]Bain & Company (2026). How India shops online 2026 [Report prepared in collaboration with Flipkart]. Bain & Company. https://www.bain.com/insights/how-india-shops-online-2026/
2. [2]Blut M, Chaney D, Lunardo R, Mencarelli R, Grewal D (2024). Customer perceived value: A comprehensive meta-analysis. Journal of Service Research, 27(4), 501–524. https://doi.org/10.1177/10946705231222295
3. [3]Chavadi CA, Kokatnur SS, Sirothiya M (2024). Role of Q-commerce instant gratification on customer satisfaction: The moderating effect of green packaging. Indian Journal of Marketing, 54(8), 30–50. https://doi.org/10.17010/ijom/2024/v54/i8/174185
4. [4]Davis FD (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
5. [5]Dunlap RE, Van Liere KD, Mertig AG, Jones RE (2000). Measuring endorsement of the new ecological paradigm: A revised NEP scale. Journal of Social Issues, 56(3), 425–442. https://doi.org/10.1111/0022-4537.00176
6. [6]Farhat R, Yang Q, Ahmed MAO, Hasan G (2025). E-commerce for a sustainable future: Integrating trust, product quality perception, and online-shopping satisfaction. Sustainability, 17(4), Article 1431. https://doi.org/10.3390/su17041431
7. [7]Hair JF Jr, Hult GTM, Ringle CM, Sarstedt M (2022). A primer on partial least squares structural equation modeling (PLS-SEM), 3rd ed. Sage, Thousand Oaks, CA.
8. [8]Hair JF Jr, Hult GTM, Ringle CM, Sarstedt M, Danks NP, Ray S (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer, Cham. https://doi.org/10.1007/978-3-030-80519-7
9. [9]Hasan MB, Verma R, Sharma D, Moghalles SAM, Hasan SAS (2024). The impact of environmental, social, and governance (ESG) practices on customer behavior towards the brand in light of digital transformation: Perceptions of university students. Cogent Business & Management, 11(1), Article 2371063. https://doi.org/10.1080/23311975.2024.2371063
10. [10]Haws KL, Winterich KP, Naylor RW (2014). Seeing the world through GREEN-tinted glasses: Green consumption values and responses to environmentally friendly products. Journal of Consumer Psychology, 24(3), 336–354. https://doi.org/10.1016/j.jcps.2013.11.002
11. [11]Henseler J, Ringle CM, Sarstedt M (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
12. [12]Hu L, Bentler PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
13. [13]Hudayah S, Ramadhani MA, Sary KA, Raharjo S, Yudaruddin R (2023). Green perceived value and green product purchase intention of Gen Z consumers: Moderating role of environmental concern. Environmental Economics, 14(2), 87–102. https://doi.org/10.21511/ee.14(2).2023.07
14. [14]Kim HW, Chan HC, Gupta S (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111–126. https://doi.org/10.1016/j.dss.2005.05.009
15. [15]Kıymalıoğlu A, Yetkin Özbük RM, Aydın Ünal D, Dirlik O, Akar N (2024). Unpacking sustainable packaging through the stimulus-organism-response model: A systematic literature review. SAGE Open, 14(4). https://doi.org/10.1177/21582440241302320
16. [16]Kock N, Hadaya P (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131
17. [17]Miao M, Jalees T, Zaman SI, Khan S, Hanif N-A, Javed MK (2022). The influence of e-customer satisfaction, e-trust and perceived value on consumer’s repurchase intention in B2C e-commerce segment. Asia Pacific Journal of Marketing and Logistics, 34(10), 2184–2206. https://doi.org/10.1108/APJML-03-2021-0221
18. [18]Mume AH, Sun X (2026). Driving sustainable consumption online: The interplay of green digital marketing and digital literacy in shaping purchase intentions for sustainable coffee in urban Ethiopia’s e-commerce. Journal of Theoretical and Applied Electronic Commerce Research, 21(1), Article 25. https://doi.org/10.3390/jtaer21010025
19. [19]Panesar A, Kakkar A, Sood R, Santos JD (2026). Understanding Q-commerce adoption: The role of technological, social, marketing stimuli, and service quality on usage intention. European Journal of Management Studies. Advance online publication. https://doi.org/10.1108/EJMS-06-2025-0079
20. [20]Pérez A, Rodríguez del Bosque I (2013). Measuring CSR image: Three studies to develop and to validate a reliable measurement tool. Journal of Business Ethics, 118(2), 265–286. https://doi.org/10.1007/s10551-012-1588-8
21. [21]Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
22. [22]Rana S, Shafi F, Rasheed A, Malik MI (2024). Online environmental platforms service and green consumer behavior nexus: A multi-mediator study. Future Business Journal, 10, Article 3. https://doi.org/10.1186/s43093-023-00283-4
23. [23]Ringle CM, Wende S, Becker J-M (2024). SmartPLS 4 [Computer software]. SmartPLS, Bönningstedt. https://www.smartpls.com
24. [24]Sarstedt M, Ringle CM, Hair JF (2022). Partial least squares structural equation modeling. In: Homburg C, Klarmann M, Vomberg A (eds.), Handbook of market research, 587–632. Springer, Cham. https://doi.org/10.1007/978-3-319-57413-4_15
25. [25]Schoenmaker D, Schramade W (2019). Principles of sustainable finance. Oxford University Press, Oxford.
26. [26]Shih IT, Silalahi ADK, Baljir K, Jargalsaikhan S (2024). Exploring the impact of perceived sustainability on customer satisfaction and the mediating role of perceived value. Cogent Business & Management, 11(1), Article 2431647. https://doi.org/10.1080/23311975.2024.2431647
27. [27]Siddiqi KO, Rahman J, Tanchangya T, Rahman H, Esquivias MA, Rahman MH (2024). Investigating the factors influencing customer loyalty and the mediating effect of customer satisfaction in online food delivery services: Empirical evidence from an emerging market. Cogent Business & Management, 11(1), Article 2431188. https://doi.org/10.1080/23311975.2024.2431188
28. [28]Sweeney JC, Soutar GN (2001). Consumer perceived value: The development of a multiple item scale. Journal of Retailing, 77(2), 203–220. https://doi.org/10.1016/S0022-4359(01)00041-0
29. [29]Turker D (2009). Measuring corporate social responsibility: A scale development study. Journal of Business Ethics, 85(4), 411–427. https://doi.org/10.1007/s10551-008-9780-6
30. [30]Venkatesh V, Morris MG, Davis GB, Davis FD (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
31. [31]Yum K, Kim J (2024). The influence of perceived value, customer satisfaction, and trust on loyalty in entertainment platforms. Applied Sciences, 14(13), Article 5763. https://doi.org/10.3390/app14135763
32. [32]Zeithaml VA (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.1177/002224298805200302
33. [33]Zhao X, Lynch JG Jr, Chen Q (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257