Digital Transformation Through Online Shopping: Assessing Its Contribution To Green Technology Adoption And Sustainable Consumption

Source: Author's compilation based on the reviewed literature (2020–2026).

Table:1 Brief Review of Literature on Online Shopping Digitalisation, Green Technology Adoption and Sustainable Consumption

Recent research highlights the growing interrelationship between digital transformation, environmental sustainability, green technology and responsible consumer behaviour. Poojari N. (2026) examined digital inclusion, rural youth capabilities and inclusive growth, highlighting the broader role of digital transformation in enhancing capabilities and supporting inclusive development, while Poojari N. (2026) also examined digital transformation and rural youth empowerment, providing a relevant foundation for understanding how digital technologies can influence socio-economic behaviour and development. In the area of environmental sustainability, Poojari N. (2026) examined sustainable green entrepreneurship and the role of green product innovation, thereby emphasising the importance of green innovation in achieving sustainable economic outcomes. Similarly, Poojari N. (2024) examined the role of organic products in achieving a sustainable green economy and the relationship between consumers’ environmental and health concerns and the use of green products (Poojari, N. 2024). Earlier studies by Poojari N. (2019) addressed consumer decision-making regarding solar energy usage and environmental protection, while Poojari N. (2018) examined economic and environmental sustainability, providing further conceptual support for linking technology adoption, environmental awareness and sustainable behaviour. Building on these strands of literature, the present study extends the discussion to the online shopping environment by examining whether Online Shopping Digitalisation (OSD) contributes to Green Technology Adoption (GTA) and Sustainable Consumption (SC), and whether GTA functions as a mediating mechanism between OSD and SC. Methodologically, the study adopts a quantitative, cross-sectional research design and collects primary data from online consumers using a structured questionnaire based on a five-point Likert scale. The relationships among OSD, GTA and SC are examined using descriptive statistics, reliability analysis, Pearson correlation and regression analysis in SPSS, followed by Partial Least Squares Structural Equation Modelling (PLS-SEM) using SmartPLS to assess the measurement model, structural relationships and the mediating effect of GTA. This approach enables the study to examine both the direct relationship between digitalised online shopping and sustainable consumption and the indirect pathway through green technology adoption.

5. RESEARCH GAP:

• Limited integration of digitalisation and sustainability: Existing studies have examined online shopping, e-commerce sustainability and green consumption separately, but fewer studies integrate these dimensions into a single empirical framework.
• Limited consumer-level evidence: Much of the existing research focuses on e-commerce supply chains, logistics, retailers, platforms or macro-level regional data, rather than individual consumers.
• Green Technology Adoption is underexplored: Although digital technologies can facilitate environmentally responsible practices, relatively limited attention has been given to Green Technology Adoption (GTA) as a distinct consumer-level construct.
• Insufficient examination of the OSD–GTA–SC relationship: The relationship between Online Shopping Digitalisation (OSD), Green Technology Adoption (GTA), and Sustainable Consumption (SC) has not been sufficiently examined within an integrated model.
• Limited mediation analysis: Previous studies have largely focused on direct relationships, while the mediating role of Green Technology Adoption between online shopping digitalisation and sustainable consumption remains insufficiently investigated.
• Indian consumer evidence is limited: Although some studies have examined sustainable e-commerce practices and green purchase intentions in India, there is limited evidence explaining the broader digitalisation–green technology–sustainable consumption mechanism among Indian online consumers.
• Need for empirical PLS-SEM evidence: There is scope for a comprehensive PLS-SEM model that simultaneously examines the direct effects of online shopping digitalisation and green technology adoption and the indirect effect through mediation.

Therefore, the present study addresses the identified gap by developing and empirically testing a consumer-level PLS-SEM framework in which Green Technology Adoption is proposed as a mediating mechanism linking Online Shopping Digitalisation with Sustainable Consumption.

6. STATEMENT OF THE PROBLEM

The rapid expansion of online shopping has significantly transformed consumer purchasing behaviour through digital platforms, mobile applications, digital payments, personalised recommendations, and technology-enabled services. While such digitalisation offers greater convenience, accessibility, and information, its contribution to green technology adoption and sustainable consumption remain inadequately understood. Online shopping may facilitate environmentally responsible choices through digital receipts, eco-friendly product information, sustainable delivery options, and technology-supported recycling systems; however, it may also increase packaging waste, frequent deliveries, product returns, and excessive consumption. Existing studies have largely examined e-commerce sustainability, green purchasing behaviour, digital technologies, and environmental practices independently, leaving limited evidence on their integrated consumer-level relationships. Particularly, the mechanism through which Online Shopping Digitalisation (OSD) influences Sustainable Consumption (SC) through Green Technology Adoption (GTA) requires further empirical investigation. Therefore, this study employs PLS-SEM to examine these relationships and determine whether Green Technology Adoption mediates the relationship between Online Shopping Digitalisation and Sustainable Consumption.

7. SCOPE OF THE STUDY

The study focuses on online consumers and examines how Online Shopping Digitalisation (OSD) influences Green Technology Adoption (GTA) and Sustainable Consumption (SC). It covers consumers' use of digital shopping platforms, technology-enabled green practices and environmentally responsible purchasing behaviour. The study specifically investigates the direct relationships among OSD, GTA and SC and the mediating role of GTA in the relationship between OSD and SC. Primary data will be collected from online shopping consumers through a structured questionnaire and analysed using descriptive statistics and PLS-SEM. The study is limited to consumer-level perceptions and behaviours associated with online shopping and does not examine retailer-level supply chains, logistics or broader macroeconomic effects.

8. JUSTIFICATION OF THE STUDY

The study is justified by the growing integration of digital transformation and environmental sustainability in e-commerce. Although previous research has examined online shopping and green consumption, limited attention has been given to the mechanism through which digitalisation may encourage green technology adoption and sustainable consumption. Examining GTA as a mediating variable provides a more comprehensive understanding of this relationship. The findings can contribute to academic literature and provide useful insights for e-commerce platforms, consumers and sustainability-oriented policymakers seeking to promote environmentally responsible digital consumption.

8. SIGNIFICANCE OF THE STUDY:

The study is significant because it examines the growing intersection of digital transformation and environmental sustainability in online shopping. It contributes to academic literature by integrating Online Shopping Digitalisation, Green Technology Adoption and Sustainable Consumption within a single empirical framework. The study is particularly relevant because it examines Green Technology Adoption as a mediating mechanism, providing a deeper understanding of how digitalised shopping environments may influence sustainable consumer behaviour.

The findings may help e-commerce platforms and digital retailers understand how technology-enabled green practices can encourage environmentally responsible consumption. For consumers, the study may increase awareness of the environmental implications of digital purchasing and the role of green technologies. For policymakers and sustainability practitioners, the findings can provide empirical insights for developing strategies that encourage sustainable digital commerce. Methodologically, the application of PLS-SEM provides empirical evidence on both direct and indirect relationships among the study variables.

9. THEORETICAL AND CONCEPTUAL FRAMEWORK

1. Theoretical Framework: The study can be grounded in Unified Theory of Acceptance and Use of Technology (UTAUT) and Value–Belief–Norm (VBN) Theory to explain the relationship between digitalised online shopping, green technology adoption and sustainable consumption.

Unified Theory of Acceptance and Use of Technology (UTAUT) : UTAUT provides a theoretical basis for understanding consumers' acceptance and use of digital technologies. In the present study, the theory helps explain how consumers' interaction with online shopping platforms, digital payments, mobile applications, digital information and technology-enabled services contributes to the adoption of green technologies. Higher acceptance and use of digital technologies may create opportunities for consumers to engage with environmentally oriented digital services.

Thus, UTAUT provides theoretical support for the relationship: Online Shopping Digitalisation → Green Technology Adoption

Value–Belief–Norm (VBN) Theory : VBN theory explains environmentally responsible behaviour through individuals' values, environmental beliefs and personal norms. In the context of this study, consumers who recognise environmental consequences may be more willing to adopt green technologies and engage in environmentally responsible consumption practices.

Thus, VBN provides theoretical support for: Green Technology Adoption → Sustainable Consumption

It also helps explain how digital environments can provide information and opportunities that facilitate environmentally responsible consumer behaviour.

2. Conceptual Framework

The conceptual framework integrates Online Shopping Digitalisation (OSD), Green Technology Adoption (GTA), and Sustainable Consumption (SC).

Online Shopping Digitalisation (OSD) : OSD represents consumers' use of digital platforms and technologies for online purchasing, including digital payments, mobile applications, online product information, reviews and personalised digital services.

 Green Technology Adoption (GTA) :  GTA represents consumers' willingness to adopt or support technology-enabled environmentally friendly practices associated with online shopping, such as paperless transactions, digital receipts, green delivery options, recycling and technology-enabled returns.

2.3 Sustainable Consumption (SC): SC represents environmentally responsible consumer behaviour, including preference for eco-friendly products, reduced waste, consideration of product durability and packaging, and environmental considerations in purchasing decisions.

2.4 Mediating Role of Green Technology Adoption: The framework proposes that OSD may influence SC directly and indirectly through GTA. GTA therefore functions as the mediating variable.

10. Objective

1. To examine the relationship between online shopping digitalisation, green technology adoption and sustainable consumption among online consumers.
2. To assess the mediating role of green technology adoption in the relationship between online shopping digitalisation and sustainable consumption

11. Research Hypotheses:

H1: Online Shopping Digitalisation has a significant positive effect on Green Technology Adoption.

H2: Online Shopping Digitalisation has a significant positive effect on Sustainable Consumption.

H3: Green Technology Adoption has a significant positive effect on Sustainable Consumption.

H4: Green Technology Adoption significantly mediates the relationship between Online Shopping Digitalisation and Sustainable Consumption.

12. RESEARCH METHODOLOGY:

The present study adopts a quantitative, cross-sectional research design to examine the relationship between Online Shopping Digitalisation (OSD), Green Technology Adoption (GTA), and Sustainable Consumption (SC) among online consumers. Primary data were collected from respondents having experience with online shopping through a structured questionnaire comprising demographic variables and 30 measurement items covering OSD, GTA, and SC, measured on a five-point Likert scale ranging from 1 = Strongly Disagree to 5 = Strongly Agree. The study uses 220 valid responses for the empirical analysis. Descriptive statistics were employed to understand the central tendency and dispersion of the major constructs, while Cronbach’s alpha was used to assess the internal consistency of the measurement items.

Pearson correlation analysis was conducted to examine the associations among OSD, GTA, and SC. Subsequently, simple linear regression analysis using SPSS was employed to test the direct relationships corresponding to H1 (OSD → GTA), H2 (OSD → SC), and H3 (GTA → SC). The results indicate statistically significant positive relationships for all three paths, with OSD explaining 6.8% of the variance in GTA, OSD explaining 8.9% of the variance in SC, and GTA explaining 21.6% of the variance in SC. To examine the proposed mediating role of GTA in the relationship between OSD and SC (H4), the study further proposes Partial Least Squares Structural Equation Modelling (PLS-SEM) using SmartPLS, including assessment of the measurement model, structural model, path coefficients, coefficient of determination (R²), and bootstrapped indirect effect with confidence intervals. Thus, the methodology integrates SPSS-based statistical analysis with PLS-SEM to assess both the direct and indirect relationships among digitalised online shopping, green technology adoption, and sustainable consumption.

13. DATA ANALYSIS OF THE STUDY:

Table: 2-Experience with Online Shopping by Frequency of Online Shopping

Table 2 presents the distribution of respondents according to their online shopping experience and frequency of online shopping. Among respondents shopping 3–5 times a month, those with less than one year of experience constituted the largest group (31%), followed by those with 4–6 years of experience (26%). Among respondents shopping 6–10 times a month, the highest proportion had less than one year of experience (35%), followed by 4–6 years (27%). In the more than 10 times a month category, respondents with 1–3 years of experience formed the largest proportion (36%), followed by those with less than one year (33%). Overall, the data indicate considerable variation in online shopping frequency across different levels of shopping experience, with frequent online shopping reported by consumers from both relatively new and more experienced user groups.

Table: 3- Gender-wise Distribution of Respondents by Frequency of Online Shopping

Table 3 presents the gender-wise distribution of respondents according to their frequency of online shopping. Among male respondents, the highest proportion (37%) reported shopping online 3–5 times a month, followed by 29% who shopped 6–10 times a month.Similarly, among female respondents, the largest proportion (43%) reported shopping 3–5 times a month, followed by 26% who shopped 6–10 times a month. Only a small proportion of both male (3%) and female (4%) respondents reported shopping online less than once a monthOverall, the distribution indicates that 3–5 online shopping occasions per month was the most common frequency among both male and female respondents.

Table 4: Age-wise Distribution of Respondents by Experience with Online Shopping

Table 4 presents the age-wise distribution of respondents according to their experience with online shopping. Among respondents aged 18–25 years, the largest proportion (48%) had less than one year of online shopping experience. In the 26–35 years group, less than one year of experience was also the largest category (35%), while among respondents aged 36–45 years, 1–3 years of experience accounted for the highest proportion (30%). For the 46–55 years group, 4–6 years of experience was the largest category (29%). Among respondents aged above 55 years, both less than one year and 1–3 years of experience accounted for 30% each. Overall, the table indicates variation in online shopping experience across age groups, with relatively shorter experience being more prominent among younger respondents.

Table 5: Educational Qualification-wise Distribution of Respondents by Experience with Online Shopping

Table 5 presents the distribution of respondents according to educational qualification and experience with online shopping. Among respondents with Higher Secondary education, the largest proportion (42%) had 1–3 years of online shopping experience. Similarly, among undergraduate respondents, 30% had 1–3 years of experience, while postgraduate respondents recorded the highest proportion (34%) in the same category. Among respondents with M.Phil./Ph.D. qualifications, 33% had 1–3 years of experience. Overall, 1–3 years of online shopping experience was the most prominent category across all educational groups, indicating that this level of experience was relatively common irrespective of educational qualification.

Table 6: Occupation-wise Distribution of Respondents by Experience with Online Shopping

Interpretation: Table 6 presents the occupational distribution of respondents according to their experience with online shopping. Among students, the highest proportion (75%) reported 4–6 years of online shopping experience. For government employees, 44% had 1–3 years of experience. Among private employees, the largest proportion (38%) also reported 1–3 years of experience. For self-employed/business respondents, 35% had 1–3 years of experience, while among professionals, 31% reported 4–6 years of experience. Overall, the distribution shows variation in online shopping experience across occupational groups, with 1–3 years and 4–6 years emerging as the more prominent experience categories.

Table 7: Monthly Household Income-wise Distribution of Respondents by Experience with Online Shopping

Table 7 presents the distribution of respondents according to monthly household income and experience with online shopping. Among respondents with a monthly household income below ₹25,000, the largest proportion (39%) had 1–3 years of online shopping experience. Similarly, among those earning ₹25,001–₹50,000, 29% reported 4–6 years of experience, while 1–3 years was the largest category among respondents earning ₹50,001–₹75,000 (38%), ₹75,001–₹1,00,000 (50%), and above ₹1,00,000 (43%). Overall, the findings indicate that 1–3 years of online shopping experience was particularly prominent among respondents in the higher income groups, while experience varied across income categories.

Table 8: Reliability Statistics of the Overall Research Scale

Table 8 presents the reliability statistics for the overall 37-item research scale. The Cronbach’s Alpha value of 0.616 indicates a moderate level of internal consistency among the items. The scale has a mean score of 118.5227 and a standard deviation of 13.16889, with a variance of 173.420. The obtained reliability coefficient suggests that the items demonstrate an acceptable degree of consistency for preliminary analysis; however, the reliability of the individual constructs OSD, GTA and SC should also be assessed separately before conducting the final PLS-SEM analysis.

Table 9: Descriptive Statistics and Pearson Correlations among OSD, GTA and SC

Interpretation: Table 9 presents the descriptive statistics and Pearson correlation coefficients among Online Shopping Digitalisation (OSD), Green Technology Adoption (GTA), and Sustainable Consumption (SC) based on 220 respondents. The mean scores for OSD (3.3759), GTA (3.3273), and SC (3.3455) indicate moderate levels across the three constructs. The correlation results show that OSD has a positive and statistically significant relationship with GTA (r = 0.261, p < 0.01) and SC (r = 0.299, p < 0.01). GTA also demonstrates a positive and statistically significant relationship with SC (r = 0.465, p < 0.01). These findings indicate that the three constructs are positively associated, providing preliminary empirical support for the proposed relationships among OSD, GTA, and SC. However, correlation results alone do not establish causal effects or confirm the hypotheses; the proposed direct and mediation effects should be tested using regression or PLS-SEM.

Table 10 presents the multiple regression model in which Online Shopping Digitalisation (OSD) is the dependent variable and Green Technology Adoption (GTA) and Sustainable Consumption (SC) are the predictors. The model reports an R value of 0.329 and an R² of 0.108, indicating that GTA and SC jointly explain 10.8% of the variation in OSD.

Table 10: Multiple Regression Model Summary of Green Technology Adoption and Sustainable Consumption on Online Shopping Digitalisation

The adjusted R² is 0.100, while the overall regression model is statistically significant (F = 13.174, p < 0.001). The Durbin–Watson value of 1.591 indicates that the residuals do not show an extreme departure from independence. However, because the proposed conceptual framework specifies OSD → GTA → SC, this reverse-direction regression should not be used as the primary test of H1–H4. The hypotheses should instead be examined using the theoretically specified regression paths or, preferably, PLS-SEM with GTA tested as the mediator.

Table 11: ANOVA Results for the Regression Model Predicting Online Shopping Digitalisation

Table 11 presents the ANOVA results for the multiple regression model examining the combined relationship of Green Technology Adoption (GTA) and Sustainable Consumption (SC) with Online Shopping Digitalisation (OSD). The regression sum of squares is 7.686, compared with a residual sum of squares of 63.297. The overall model is statistically significant (F = 13.174, p < 0.001), indicating that GTA and SC, when considered jointly, significantly explain variation in OSD in this regression model. However, as the proposed conceptual framework specifies OSD as the predictor of GTA and SC, this ANOVA result should be treated as a preliminary/reverse-direction analysis rather than as evidence for H1–H4. The hypothesis testing should follow the proposed OSD → GTA → SC model.

Table 12: Regression Coefficients and Collinearity Diagnostics for Online Shopping Digitalisation

Interpretation: Table 12 presents the regression coefficients and collinearity diagnostics for the model in which Online Shopping Digitalisation (OSD) is the dependent variable and Green Technology Adoption (GTA) and Sustainable Consumption (SC) are predictors. Both GTA (B = 0.156, β = 0.155, p = 0.003) and SC (B = 0.245, β = 0.227, p = 0.002) show statistically significant positive coefficients in this model. The collinearity statistics indicate Tolerance = 0.784 and VIF = 1.276 for both predictors, suggesting no serious multicollinearity problem. The maximum condition index is 15.675, which also does not indicate severe multicollinearity. However, because this regression specifies OSD as the dependent variable, these results should not be interpreted as direct evidence for H1–H4. The proposed hypothesis model requires testing the paths OSD → GTA, OSD → SC, GTA → SC, and the indirect effect OSD → GTA → SC.

Table 13: Regression Model Summary of Online Shopping Digitalisation on Green Technology Adoption

Table 13 presents the regression model examining the effect of Online Shopping Digitalisation (OSD) on Green Technology Adoption (GTA). The model reports an R value of 0.261 and an R² value of 0.068, indicating that OSD explains 6.8% of the variance in GTA. The adjusted R² is 0.064, while the model is statistically significant (F = 15.907, p < 0.001). The Durbin–Watson value of 1.578 indicates that the residuals do not show a substantial departure from independence. These results provide preliminary statistical support for the proposed H1 relationship between OSD and GTA; however, the direction and magnitude of the effect should be confirmed from the corresponding regression coefficients and, for the final study, through the PLS-SEM structural model.

Table 14: ANOVA Results for the Effect of Online Shopping Digitalisation on Green Technology Adoption

Table 14 presents the ANOVA results for the regression model examining the effect of Online Shopping Digitalisation (OSD) on Green Technology Adoption (GTA). The regression sum of squares is 4.793, while the residual sum of squares is 65.684. The model produces an F-value of 15.907 with p < 0.001, indicating that the regression model is statistically significant. Thus, OSD significantly explains variation in GTA in the present sample. This finding provides preliminary support for the proposed H1, which states that Online Shopping Digitalisation has a significant positive effect on Green Technology Adoption. The direction and strength of the relationship should be established from the corresponding coefficient results.

Table 15: Regression Coefficients for the Effect of Online Shopping Digitalisation on Green Technology Adoption

Table 15 presents the regression coefficients for the effect of Online Shopping Digitalisation (OSD) on Green Technology Adoption (GTA). The results show a positive and statistically significant relationship between OSD and GTA (B = 0.260, β = 0.261, t = 3.988, p < 0.001). This indicates that an increase in OSD is associated with an increase in GTA. The model has a tolerance value of 1.000 and a VIF of 1.000, indicating no multicollinearity concern. Therefore, the coefficient results provide statistical support for H1, which proposes that Online Shopping Digitalisation has a significant positive effect on Green Technology Adoption.

Table 16: Regression Model Summary of Online Shopping Digitalisation on Sustainable Consumption

Table 16 presents the regression model examining the effect of Online Shopping Digitalisation (OSD) on Sustainable Consumption (SC). The model reports R = 0.299 and R² = 0.089, indicating that OSD explains approximately 8.9% of the variation in Sustainable Consumption. The adjusted R² is 0.085, while the overall regression model is statistically significant (F = 21.388, p < 0.001). The Durbin–Watson value of 1.421 indicates no substantial concern regarding residual independence. These findings provide preliminary evidence of a statistically significant relationship between OSD and SC and are consistent with the proposed direction of H2. However, the corresponding regression coefficient should be examined to establish whether the effect is specifically positive, as stated in H2.

Table 17: ANOVA Results for the Effect of Online Shopping Digitalisation on Sustainable Consumption

Table 17 presents the ANOVA results for the regression model examining the effect of Online Shopping Digitalisation (OSD) on Sustainable Consumption (SC). The regression sum of squares is 5.424, while the residual sum of squares is 55.282. The model is statistically significant (F = 21.388, p < 0.001), indicating that OSD significantly explains variation in SC among the respondents. This finding provides statistical evidence in support of the proposed relationship between OSD and SC. However, the direction of the effect should be confirmed from the regression coefficient table before making the final decision on H2, which specifically proposes a positive effect.

Table 18: Regression Coefficients for the Effect of Online Shopping Digitalisation on Sustainable Consumption

Table 18 presents the regression coefficients for the effect of Online Shopping Digitalisation (OSD) on Sustainable Consumption (SC). The results indicate a positive and statistically significant effect of OSD on SC (B = 0.276, β = 0.299, t = 4.625, p < 0.001). This suggests that higher levels of online shopping digitalisation are associated with higher levels of sustainable consumption among the respondents. The tolerance value (1.000) and VIF (1.000) indicate that multicollinearity is not a concern in this model. Therefore, the regression results provide statistical support for H2, which states that Online Shopping Digitalisation has a significant positive effect on Sustainable Consumption.

Table 19: Model Summary of Green Technology Adoption on Sustainable Consumption

Table 19 presents the regression model examining the effect of Green Technology Adoption (GTA) on Sustainable Consumption (SC) among the respondents. The model reports an R value of 0.465 and an R² value of 0.216, indicating that GTA explains 21.6% of the variation in Sustainable Consumption. The Adjusted R² of 0.213 indicates that approximately 21.3% of the variation remains explained after adjustment for the model. The regression model is statistically significant, with an F-value of 60.102 and p < 0.001, confirming a statistically significant relationship between GTA and SC. The Durbin–Watson value of 1.597 indicates that there is no substantial concern regarding residual independence. Overall, the results provide empirical support for examining H3: Green Technology Adoption → Sustainable Consumption, although the regression result represents the direct GTA–SC relationship and does not by itself establish the mediation effect proposed in H4.

Table 19: Regression Model Summary of Green Technology Adoption on Sustainable Consumption

Table 19 presents the regression model examining the effect of Green Technology Adoption (GTA) on Sustainable Consumption (SC). The model reports R = 0.465 and R² = 0.216, indicating that GTA explains approximately 21.6% of the variation in Sustainable Consumption. The adjusted R² is 0.213, while the regression model is statistically significant (F = 60.102, p < 0.001). The Durbin–Watson value of 1.597 indicates no substantial concern regarding residual independence. These findings provide evidence of a statistically significant relationship between GTA and SC and are consistent with the proposed direction of H3. The corresponding regression coefficient should be considered to confirm the positive direction and establish the final decision regarding H3.

Table 21: Regression Coefficients for the Effect of Green Technology Adoption on Sustainable Consumption

Table 21 presents the regression coefficients for the effect of Green Technology Adoption (GTA) on Sustainable Consumption (SC). The results indicate a positive and statistically significant effect of GTA on SC (B = 0.431, β = 0.465, t = 7.753, p < 0.001). This indicates that higher levels of green technology adoption are associated with higher levels of sustainable consumption among the respondents. The tolerance value (1.000) and VIF (1.000) indicate the absence of multicollinearity concerns in the model. Therefore, the findings provide statistical support for H3, which proposes that Green Technology Adoption has a significant positive effect on Sustainable Consumption.

Table 22: Descriptive Statistics of Online Shopping Digitalisation (OSD) among Respondents (N = 120)

Table presents the descriptive statistics of the Online Shopping Digitalisation (OSD) construct based on 120 respondents. The mean scores for all ten statements range from 3.0636 to 3.6773, indicating a generally moderate to positive perception of online shopping digitalisation. Among the items, OSD9—“Digital technology has increased my online shopping frequency”—records the highest mean score (3.6773; SD = 1.27513), suggesting that digital technologies have notably increased respondents’ frequency of online shopping. This is followed by OSD5 on the use of mobile apps/digital platforms for online purchases (Mean = 3.4909; SD = 1.44769) and OSD2 on using digital platforms to search for products (Mean = 3.4318; SD = 1.49882). In contrast, OSD7—use of online reviews and ratings for purchase decisions—has the lowest mean score (3.0636; SD = 1.43840), indicating comparatively lower agreement with this statement. The standard deviations range from 1.27513 to 1.53343, showing a moderate degree of variation in respondents’ perceptions. Overall, the findings suggest that respondents demonstrate a moderately positive level of online shopping digitalisation, particularly in terms of increased shopping frequency, mobile/digital platform usage, and digital product search.

Table 22: Descriptive Statistics of Green Technology Adoption (GTA) among Respondents (N = 120)

Table 22 presents the descriptive statistics of Green Technology Adoption (GTA) based on the mean and standard deviation of ten statements. The mean scores range from 2.9364 to 3.5364, indicating a generally moderate level of agreement among respondents towards the adoption of green technologies in online shopping. The highest mean score is recorded for GTA8, “I consider environmental features when choosing digital shopping platforms” (Mean = 3.5364; SD = 1.36945), suggesting that environmental considerations are relatively important in respondents’ choice of digital shopping platforms. This is followed by GTA1, willingness to use technologies that reduce the environmental impact of online shopping (Mean = 3.4318; SD = 1.41740), and GTA7, willingness to use digital tools to identify eco-friendly products (Mean = 3.4273; SD = 1.37465). In contrast, GTA3, willingness to use technologies that reduce paper use, records the lowest mean score (2.9364; SD = 1.43522), indicating comparatively lower agreement with this aspect. The remaining statements have mean scores between 3.1955 and 3.4136, reflecting moderate acceptance of eco-friendly delivery, digital receipts, recycling and return systems, energy-efficient technologies, packaging-waste reduction, and green technology adoption in e-commerce. The standard deviations range from 1.35187 to 1.56208, indicating a moderate variation in respondents’ perceptions. Overall, the findings suggest that respondents demonstrate a moderately positive orientation towards Green Technology Adoption, particularly regarding environmental considerations in digital shopping and the use of technologies that reduce environmental impacts.

Table 23: Descriptive Statistics of Sustainable Consumption (SC) among Respondents (N = 120)

Table 23 presents the descriptive statistics of Sustainable Consumption (SC) among the respondents based on ten statements. The mean scores range from 2.97 to 3.57, indicating a generally moderate level of agreement towards sustainable consumption practices in the context of online shopping. The highest mean score is observed for SC9, “I am willing to pay more for eco-friendly products” (Mean = 3.57; SD = 1.30), followed closely by SC4, “I consider product durability before buying online” (Mean = 3.56; SD = 1.32), suggesting relatively stronger sustainable consumption preferences in these areas. SC2, preference for eco-friendly products, also records a comparatively high mean (3.44; SD = 1.42). In contrast, SC6, consideration of energy efficiency when purchasing electronic products online, has the lowest mean (2.97; SD = 1.36), indicating comparatively lower agreement with this practice. The remaining statements show mean values between 3.15 and 3.40, reflecting moderate agreement with environmentally responsible purchasing, waste reduction, recyclable packaging, and awareness of sustainable products. The standard deviations range from 1.30 to 1.49, indicating moderate variation in respondents’ responses. Overall, the findings indicate a moderately positive level of sustainable consumption behaviour among the respondents, with greater emphasis on product durability and willingness to pay for eco-friendly products.

Table 22:  The hypothesis testing results indicate that H1, H2 and H3 are supported, while H4 requires further mediation analysis before a final decision can be made. For H1, the relationship between Online Shopping Digitalisation (OSD) and Green Technology Adoption (GTA) is positive and statistically significant (B = 0.260, β = 0.261, t = 3.988, p < 0.001; R² = 0.068), indicating that higher OSD is associated with higher GTA. For H2, OSD has a positive and significant relationship with Sustainable Consumption (SC) (B = 0.276, β = 0.299, t = 4.625, p < 0.001; R² = 0.089), indicating that digitalised online shopping is associated with higher sustainable consumption. For H3, GTA has a positive and statistically significant relationship with SC (B = 0.431, β = 0.465, t = 7.753, p < 0.001; R² = 0.216), showing that greater adoption of green technology is associated with stronger sustainable consumption behaviour. H4 proposes that GTA mediates the relationship between OSD and SC, meaning that OSD may influence SC partly through GTA; although the two individual paths are significant, the indirect effect and its 95% bootstrapped confidence interval have not yet been calculated, so H4 should presently be reported as “Mediation analysis required” rather than “Supported.”

Table 22: Summary of Regression Results and Hypothesis Testing