International Journal of Management Science and Information Technology IJMSIT
E-ISSN: 2774-5694
P-ISSN: 2776-7388
Volume 6 .
January-June 2026, 277-286 DOI: https://doi.
org/10.
35870/ijmsit.
The Role of Customer Experience in Shaping Purchase Decisions: The Mediating Effect of Customer Engagement on Digital E-Commerce Platforms Firdaus 1*.
Rully Sjahirul Alim 2.
Kukuh Mulyanto 3.
Abdullah 4.
Asepta Hendriyanto 5.
Nuruddin Mahmud 6.
Fiderius Ismanto 7.
Heru Yulianto 8.
Alis Arifa Rahman 9 1*,2 Department of Management.
Faculty Economic and Business.
Universitas Muhammadiyah Semarang.
Semarang City.
Central Java Province.
Indonesia 3,5,6,7,8,9 Department of Management.
Faculty Economic and Business.
Sekolah Tinggi Ilmu Ekonomi Anindyaguna.
Semarang City.
Central Java Province.
Indonesia 4 Department of Management.
Faculty Economic and Business.
Sekolah Tinggi Ilmu Ekonomi Totalwin.
Semarang City.
Central Java Province.
Indonesia Email: fir@unimus.
id 1*, rully@unimus.
id 2, kukuh@stieanindyaguna.
id 3, abdullah@stietotalwin.
id 4, asepta@stieanindyaguna.
id 5, nuruddin@stieanindyaguna.
id 6, fismanto@stieanindyaguna.
id 7, heru@stieanindyaguna.
id 8, alis@stieanindyaguna.
Abstract Article history:
Received February 19, 2026 Revised March 23, 2026 Accepted March 30, 2026 This study examines the effect of customer experience on purchase decisions, with customer engagement as a mediating variable in digital ecommerce platforms.
Increasing competition in the e-commerce sector has shifted managerial focus toward experiential and relational factors influencing consumer decision-making.
A quantitative approach was employed using survey data collected from e-commerce users through purposive sampling.
Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS.
The results show that customer experience has a significant positive effect on both customer engagement and purchase decisions.
Customer engagement also significantly mediates the relationship, indicating that improved experiences enhance purchasing outcomes indirectly through stronger However, the moderating effect between the variables is not statistically significant.
The model demonstrates satisfactory explanatory power, indicating its robustness in explaining consumer behavior in digital platforms.
These findings emphasize the importance of designing effective experience-based strategies to strengthen engagement and drive purchasing outcomes.
This study provides practical insights for ecommerce managers and contributes to the literature on digital marketing and consumer behavior.
Keywords:
Customer Experience.
Customer Engagement.
Purchase Decisions.
Digital E-Commerce.
PLS-SEM.
INTRODUCTION
The rapid development of information technology has fundamentally transformed the structure and dynamics of modern business, particularly within digital e-commerce platforms such as online marketplaces and mobile commerce applications.
In this environment, competition no longer relies solely on price or product availability but increasingly on the quality of customer experience delivered through digital interfaces, system reliability, and personalized services.
As a result, customer experience has become a critical strategic resource that shapes consumer perceptions and influences decision-making processes in technology-driven markets.
From a theoretical perspective, customer experience is conceptualized as a multidimensional construct encompassing cognitive, emotional, and behavioral responses the customer journey (Lemon & Verhoef.
Homburg et al.
further emphasize that managing customer experience is essential for creating value and sustaining competitive advantage, particularly in digital contexts where interactions are mediated Volume 6 .
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by technology.
Building on this foundation, recent studies highlight customer engagement as a key relational mechanism that links experience to behavioral outcomes.
Engagement reflects a customerAos level of interaction, participation, and emotional connection with a platform, which ultimately drives purchase-related behaviors (Brodie et al.
, 2019.
Hollebeek et al.
, 2.
Empirical evidence in digital retail and e-commerce contexts supports the argument that engagement plays a pivotal role in translating positive experiences into actual purchasing decisions.
Despite the growing body of literature, several important gaps remain.
First, prior studies predominantly examine the direct relationship between customer experience and purchase decisions, with limited attention to the mediating role of customer engagement, particularly within platform-based digital e-commerce Second, much of the existing research emphasizes branding and promotional variables such as brand image or endorsementsAiwhile insufficiently addressing how experiential factors interact with engagement mechanisms in shaping consumer behavior.
Third, empirical findings regarding the interplay between customer experience and engagement remain inconsistent, especially in emerging digital markets, suggesting the need for further empirical validation using robust analytical approaches.
Accordingly, this study focuses specifically on digital e-commerce platforms, including online marketplaces and mobile-based shopping applications, where customer interactions are highly dynamic and technology-mediated.
The central research question is: how does customer experience influence purchase decisions through customer engagement in digital e-commerce platforms? To address this question, the study aims to .
analyze the effect of customer experience on customer engagement, .
examine the direct effects of customer experience and customer engagement on purchase decisions, and .
evaluate the mediating role of customer engagement within the proposed structural model.
This study contributes to the literature in several ways.
Theoretically, it strengthens the linkage between customer experience and engagement by empirically testing an integrated model within a digital platform context, thereby addressing the identified research gaps.
It also reinforces the theoretical arguments proposed by Homburg et al.
by positioning customer experience as a driver of relational outcomes through engagement mechanisms.
Practically, the findings offer actionable insights for e-commerce managers in designing experience-oriented strategies that foster stronger customer engagement and ultimately enhance purchase decisions.
The remainder of this paper is organized as follows.
The next section presents the research methodology, followed by the results and discussion.
The final section concludes the study by summarizing key findings, outlining managerial implications, and suggesting directions for future research.
Customer Experience in Digital E-Commerce Customer experience refers to the overall cognitive, emotional, and behavioral responses arising from interactions between customers and firms across digital touchpoints (Lemon & Verhoef, 2.
In digital ecommerce platforms such as online marketplaces and mobile commerce applications customer experience is shaped by factors including system usability, information quality, responsiveness, personalization, and service reliability.
Recent technological advancements, particularly in artificial intelligence (AI) and machine learning, have further transformed how customer experience is designed and delivered.
AI-driven recommendation systems, personalized interfaces, and predictive analytics enable platforms to tailor interactions in real time, thereby enhancing perceived relevance and convenience.
These developments reinforce the strategic importance of customer experience as a determinant of consumer evaluation and decision-making in digital Prior studies indicate that a positive customer experience enhances perceived value and reduces uncertainty, leading to favorable behavioral outcomes such as trust, satisfaction, and loyalty (Homburg et al.
Hollebeek et al.
, 2.
In e-commerce contexts, seamless and personalized experiences have been shown to directly influence purchase decisions by simplifying information processing and strengthening consumer confidence.
Customer Engagement as a Relational Mechanism Customer engagement represents the level of a customerAos cognitive, emotional, and behavioral investment in interactions with a platform or brand.
Unlike purely transactional constructs, engagement reflects an ongoing relational state that develops through repeated and meaningful interactions.
In digital environments, engagement can be observed through behaviors such as content interaction, user participation, feedback provision, and continued platform usage.
The literature emphasizes customer engagement as a key mechanism linking firm-driven stimuli such as customer experience to behavioral outcomes.
Brodie et al.
and Hollebeek et al.
conceptualize engagement as a central component of value co-creation, where customers actively participate in shaping their own experiences.
Furthermore, recent studies highlight that AI-enabled features, such as personalized recommendations and interactive chatbots, can strengthen engagement by increasing interactivity and perceived responsiveness.
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Empirical evidence consistently shows that higher levels of engagement lead to stronger purchase intentions and actual purchasing behavior, particularly in digital and social commerce settings.
Customer Experience and Customer Engagement Customer experience is widely recognized as a primary antecedent of customer engagement.
Highquality digital experiences foster positive emotions, perceived control, and immersion, which encourage customers to engage more deeply with platforms.
When interactions are perceived as seamless, personalized, and enjoyable, customers are more likely to develop stronger relational bonds and actively participate in platform activities.
Recent empirical studies confirm the positive relationship between customer experience and customer engagement, particularly in technology-mediated environments where personalization and system quality play critical roles (Hollebeek et al.
, 2022.
Firdaus & Firdaus,2.
Moreover.
AI-driven personalization is increasingly seen as a reinforcing factor that amplifies the impact of experience on engagement, although its role is not always explicitly modeled in prior studies.
H1: Customer experience has a positive effect on customer engagement in digital e-commerce platforms.
Customer Experience and Purchase Decision Purchase decision represents the final stage of the consumer decision-making process, in which consumers determine whether to proceed with a transaction.
In digital e-commerce settings, purchase decisions are influenced not only by product-related attributes but also by experiential factors embedded within the platform.
A positive customer experience reduces perceived risk, enhances trust, and improves decision efficiency, thereby increasing the likelihood of purchase.
Prior research consistently demonstrates that customer experience has a direct and significant effect on purchase decisions, particularly in online retail environments where physical interaction with products is limited.
H2: Customer experience has a positive effect on purchase decisions in digital e-commerce platforms.
Customer Engagement and Purchase Decision Customer engagement is a strong predictor of customer behavioral outcomes, including purchase Engaged customers tend to exhibit higher levels of involvement, emotional attachment, and commitment, which translate into stronger purchasing behavior.
In digital environments, engagement also reflects the intensity of interaction and the depth of the customer-platform relationship.
Studies in digital marketing and social commerce contexts indicate that engaged customers are more likely to make purchases, repeat transactions, and demonstrate favorable decision outcomes.
These findings highlight the importance of engagement as a driver of behavioral conversion in e-commerce platforms.
H3:
Customer engagement has a positive effect on purchase decisions in digital e-commerce platforms.
The Mediating Role of Customer Engagement From a relational and value co-creation perspective, customer engagement is expected to mediate the relationship between customer experience and purchase decisions.
A positive experience encourages customers to become more engaged, and this engagement subsequently translates experiential value into actual purchasing behavior.
This mechanism suggests that engagement serves as a critical pathway through which experience influences outcomes.
While prior studies acknowledge this mediating role, empirical evidence in platform-based digital ecommerce contexts remains limited and sometimes inconsistent.
Additionally, emerging technologies such as AI-driven personalization may further strengthen this indirect relationship by enhancing both experience and engagement simultaneously, although their moderating role requires further investigation.
H4: Customer engagement mediates the relationship between customer experience and purchase decisions in digital ecommerce platforms.
RESEARCH METHOD
Research Design and Approach This study adopted a quantitative approach with a causal explanatory design to examine the relationships among customer experience, customer engagement, and purchase decisions in digital ecommerce platforms.
This design is appropriate for testing hypotheses and analyzing causal relationships among latent constructs using statistical modeling techniques (Creswell & Creswell, 2.
The study specifically focuses on platform-based e-commerce environments, including online marketplaces .
Shopee.
Tokopedi.
and mobile commerce applications, where customer interactions are highly technology Volume 6 .
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Population and Sample The population comprised active users of digital e-commerce platforms.
A purposive sampling technique was employed to ensure that respondents met predefined criteria, namely individuals who had conducted at least one online purchase within the previous six months and had experience interacting with platform features such as product search, recommendations, and reviews.
To improve representativeness, the sample was drawn from users with diverse demographic backgrounds, including age, gender, and frequency of online shopping.
A total of 200 valid responses were collected, which meets the minimum requirements for PLS-SEM analysis based on the Au10-times ruleAy and recent methodological recommendations (Hair et al.
However, potential demographic imbalances were acknowledged and considered when interpreting the results, particularly in relation to generalizing findings across broader populations.
Data Collection Method and Instrument Data were collected using a structured online questionnaire distributed through digital channels.
Measurement items were adapted from validated scales in prior studies on customer experience, customer engagement, and purchase decisions (Hollebeek et al.
, 2.
, ensuring content validity.
Before the main survey, a pilot test was conducted with a small group of respondents to assess clarity, reliability, and item Feedback from the pilot study was used to refine the questionnaire.
All constructs were measured using a five-point Likert scale ranging from strongly disagree to strongly agree.
Reliability and validity were assessed using CronbachAos alpha, composite reliability, and average variance extracted (AVE), following established SEM guidelines (Hair et al.
, 2.
Research Procedure The data collection process was conducted over a specified period using online distribution platforms.
Respondents were informed about the studyAos objectives and participation requirements prior to completing the questionnaire.
Participation was voluntary, and only fully completed responses were included in the Data screening procedures, including checking for missing values, outliers, and response consistency, were performed to ensure data quality prior to analysis (Field, 2.
Data Analysis Technique The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software.
PLS-SEM was selected due to its suitability for predictive research, complex models, and data that may not meet normality assumptions (Hair et al.
, 2.
The analysis followed a two-stage First, the measurement model was evaluated through indicator reliability, internal consistency reliability, convergent validity, and discriminant validity using the FornellAeLarcker criterion and the heterotrait-monotrait (HTMT) ratio (Henseler et al.
, 2.
Second, the structural model was assessed by examining path coefficients, coefficients of determination (RA), effect sizes .
A), predictive relevance (QA), and model fit indices such as the standardized root mean square residual (SRMR).
Ethical Considerations Ethical standards were strictly maintained throughout the research process.
Participation was voluntary, and informed consent was obtained prior to data collection.
To address data privacy concerns, no personally identifiable information was collected, and all responses were anonymized.
Data were securely stored and used exclusively for academic purposes.
These procedures align with ethical guidelines for social science research (Israel & Hay, 2.
Methodological Limitations This study has several limitations.
First, the use of purposive sampling may limit the generalizability of the findings, as the sample may not fully represent the broader population of e-commerce users.
Second, the relatively small sample size .
= .
, while acceptable for PLS-SEM, may reduce the robustness of results in more complex model estimations.
Third, the reliance on self-reported data introduces the potential for common method bias.
To mitigate these issues, this study employed validated measurement instruments and statistical However, these limitations may still influence the strength and generalization of the findings.
Future research is encouraged to apply probability sampling techniques, increase sample size, and incorporate longitudinal or experimental designs to enhance causal inference.
Additionally, future studies could explicitly examine the role of emerging technologies such as AI-based recommendations as moderating variables to further strengthen the model.
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RESULTS AND DISCUSSION
Descriptive Statistics and Respondent Profile A total of 200 valid responses were analyzed in this study.
Based on gender distribution, respondents consisted of 76 males .
%) and 124 females .
%).
All respondents met the purposive sampling criteria, namely active users of digital e-commerce platforms who had made at least one online purchase within the last six months.
The diversity of respondents provides an adequate representation of e-commerce users and supports the robustness of the empirical analysis.
Descriptive analysis indicates that respondents generally exhibited positive perceptions toward customer experience, customer engagement, and purchase decisions.
This suggests that experiential and relational aspects play a significant role in shaping consumer behavior within digital e-commerce Measurement Model Evaluation The measurement model was assessed to ensure reliability and validity before evaluating the structural Table 1 presents the construct reliability and convergent validity results.
Construct Customer Experience Customer Engagement Purchase Decision Table 1.
Construct Reliability and Convergent Validity CronbachAos Alpha Composite Reliability (A.
AVE The results indicate that all constructs meet the recommended thresholds for internal consistency reliability and convergent validity, as CronbachAos alpha and composite reliability values exceed the minimum acceptable level, and AVE values are above the recommended cutoff (Hair et al.
, 2.
Discriminant validity was evaluated using the FornellAeLarcker criterion and the heterotraitAemonotrait (HTMT) ratio (Table Construct Customer Engagement (CE) Customer Experience (CE.
Purchase Decision (PD) Table 2.
FornellAeLarcker Criterion CEx The square root of AVE for each construct is higher than its correlations with other constructs, indicating satisfactory discriminant validity.
However, the HTMT values for some construct pairs slightly exceed the recommended threshold, suggesting potential conceptual overlap.
This condition is still acceptable in exploratory and predictive research contexts, particularly when constructs are theoretically related (Henseler et al.
, 2.
Structural Model Results The structural model was evaluated to test the proposed hypotheses.
The modelAos explanatory power was assessed using the coefficient of determination (RA), as shown in Table 3.
Endogenous Construct Customer Engagement Purchase Decision Table 3.
Coefficient of Determination (RA) RA Adjusted RA The results indicate that customer experience explains a substantial proportion of variance in customer engagement, while customer experience and customer engagement together explain a very high proportion of variance in purchase decisions.
This demonstrates the strong predictive capability of the proposed model.
Table 4 presents the path coefficients and hypothesis testing results.
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Table 4.
Path Coefficients and Hypothesis Testing Path Path T-Statistic Coefficient Customer Experience Ie Customer Engagement Positive Significant Customer Experience Ie Purchase Decision Positive Significant Customer Engagement Ie Purchase Decision Positive Significant Customer Experience y Customer Engagement Ie Not Negative Purchase Decision Significant PValue < 0.
< 0.
< 0.
> 0.
Result Supported Supported Supported Not Supported The results confirm that customer experience has a significant positive effect on customer engagement and purchase decisions, while customer engagement also significantly influences purchase decisions.
However, the interaction effect between customer experience and customer engagement does not show a significant moderating role.
Model Fit Evaluation The overall model fit was assessed to evaluate how well the proposed structural model represents the observed data.
Model fit indices provide additional support for the adequacy of the PLS-SEM estimation, particularly in predictive oriented research.
Table 5 presents the model fit summary for both the saturated and estimated models.
Fit Index
SRMR
d_ULS Chi-square NFI Table 5.
Model Fit Summary Saturated Model Estimated Model The model fit assessment is presented in Table 5, including SRMR, d_ULS, d_G.
Chi-square, and NFI The standardized root mean square residual (SRMR) for both the saturated and estimated models is 172, which exceeds the commonly recommended threshold of 0.
08 for a good model fit (Hair et al.
, 2.
This indicates a relatively high discrepancy between the observed and predicted correlations.
However, in the context of Partial Least Squares Structural Equation Modeling (PLS-SEM), model evaluation primarily emphasizes predictive capability rather than strict global goodness-of-fit criteria.
As highlighted by Hair et al.
SRMR in PLS-SEM should be interpreted with caution, particularly in complex models involving multiple latent constructs and behavioral variables, where higher SRMR values are not uncommon.
Furthermore, the minimal differences between the saturated and estimated model values suggest that the structural model does not introduce substantial distortion to the empirical covariance structure.
The normed fit index (NFI) values .
326 and 0.
fall below the conventional threshold of 0.
90, indicating a modest Nevertheless, similar results have been reported in prior studies within digital commerce and consumer behavior research, where exploratory and prediction-oriented models tend to produce lower global fit indices.
Overall, despite the relatively low global fit indicators, the model is considered acceptable for predictive and exploratory purposes, particularly given the strong explanatory power (RA) and significant path relationships observed in the structural model.
Indicator Reliability (Outer Loading.
Indicator reliability was evaluated by examining the outer loading values of each measurement item.
Table 6 presents the standardized outer loadings for all indicators.
Indicator CE1
CE2
CE3
CE4
CEx1
CEx2
CEx3
CEx4
PD1
PD2
PD3
PD4
Table 6.
Outer Loadings of Measurement Indicators Customer Engagement Customer Experience Purchase Decision Volume 6 .
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All indicator loadings exceed the minimum recommended threshold, indicating that each item adequately represents its respective construct.
These results confirm that the measurement indicators demonstrate satisfactory reliability and contribute meaningfully to the latent variables (Hair et al.
, 2.
Discriminant Validity (HTMT Rati.
Discriminant validity was further assessed using the heterotrait monotrait (HTMT) ratio, which provides a more stringent evaluation of construct distinctiveness.
Table 7 presents the HTMT results.
Table 7.
Heterotrait Monotrait Ratio (HTMT) Construct Pair Customer Experience Ii Customer Engagement Purchase Decision Ii Customer Engagement Purchase Decision Ii Customer Experience HTMT Value The HTMT value between customer experience and customer engagement remains within the acceptable range, indicating adequate discriminant validity.
However.
HTMT values involving purchase decision slightly exceed the recommended threshold.
This suggests a high conceptual relatedness between constructs, which is theoretically plausible given the close linkage between engagement, experience, and purchasing behavior in digital e-commerce contexts.
Previous studies note that HTMT values marginally above the threshold can be tolerated when constructs are theoretically justified and supported by strong convergent validity (Henseler et al.
, 2015.
Hair et al.
, 2.
Figure 1.
Framework Discussion of Findings The findings of this study provide strong empirical evidence that customer experience serves as a fundamental antecedent of customer engagement in digital e-commerce environments.
This result reflects current market phenomena in which consumers increasingly evaluate e-commerce platforms not merely based on product availability or price, but on the quality of their end-to-end digital interactions.
Indicators such as system usability, clarity of information, responsiveness, and personalization captured within the customer experience construct play a crucial role in shaping customersAo emotional comfort and cognitive When users perceive the platform as intuitive and reliable, they are more inclined to interact, explore features, and develop a sense of connection with the platform.
This finding reinforces the argument that experience driven digital design and service quality are key triggers of engagement, consistent with prior engagement theories in technology-mediated contexts (Hollebeek et al.
, 2022.
Firdaus et al.
,2.
The significant direct effect of customer experience on purchase decisions further highlights the instrumental role of experiential quality in reducing perceived risk and decision complexity in online In e-commerce settings, where consumers cannot physically evaluate products, experiential indicators such as information accuracy, transaction security, and smooth checkout processes become decisive factors.
This result aligns with empirical findings by Martini et al.
, who emphasize that experiential cues strengthen consumer confidence and accelerate purchase decisions.
The result suggests that customer experience does not merely support engagement but also independently influences consumersAo willingness to finalize purchases.
Moreover, the strong influence of customer engagement on purchase decisions underscores engagementAos role as a relational and behavioral mechanism that converts experiential value into concrete Engagement indicators such as interaction intensity, emotional attachment, and continued platform usage signal an active relationship between customers and the e-commerce platform.
Highly engaged Volume 6 .
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customers are more likely to trust the platform, process information more deeply, and demonstrate commitment through purchasing behavior.
This finding is consistent with studies in social commerce and digital retail contexts, which identify engagement as a critical driver of purchasing outcomes (Sulistiyaningsih et al.
, 2025.
Waroka et al.
, 2.
Interestingly, the non-significant interaction effect between customer experience and customer engagement indicates that engagement does not function as a moderating variable in this model.
Instead, the findings suggest that customer engagement operates more effectively as a mediating mechanism through which customer experience influences purchase decisions.
This implies that enhancing customer experience contributes to purchasing behavior primarily by fostering deeper engagement, rather than by strengthening the direct relationship between engagement and purchase decisions.
One possible explanation for the absence of a moderating effect lies in the complexity of digital ecommerce environments, where customer behavior is shaped by multiple dynamic and technology-driven In such contexts, engagement may represent an outcome of experience rather than a boundary condition that alters its impact.
Additionally, the increasing use of AI-driven personalization, recommendation systems, and automated interactions may reduce the variability of engagement as a moderating factor, as these technologies standardize user experiences across platforms.
From a theoretical perspective, this finding refines the role of customer engagement in experiencedriven models by positioning it as a key transmission mechanism that translates experiential value into actual purchasing behavior.
Rather than amplifying or weakening relationships, engagement serves as an essential pathway linking experience to outcomes.
This clarification contributes to the ongoing discourse on customer engagement, particularly in digital e-commerce contexts where experiential, relational, and technological factors are closely intertwined.
The non-significant moderating effect suggests that customer engagement may function more as a mediating mechanism rather than a boundary condition in the relationship between customer experience and purchase decisions.
This indicates that engagement primarily serves as a pathway through which experiential value is translated into behavioral outcomes, rather than altering the strength of the relationship itself.
digital e-commerce environments, where interactions are highly structured and increasingly supported by algorithmic systems, the role of engagement may become more standardized, limiting its capacity to act as a moderating variable.
In addition, the evaluation of model fit should be interpreted within the context of PLSSEM.
Although the SRMR value in this study exceeds the commonly recommended threshold, it is important to note that an SRMR value below 0.
08 is generally considered indicative of a good model fit (Hair et al.
However.
PLS-SEM prioritizes predictive accuracy over strict model fit criteria, particularly in exploratory and complex behavioral models.
Therefore, the modelAos adequacy is better supported by its significant path relationships and explanatory power rather than solely by global fit indices.
Overall, these findings highlight that optimizing customer experience alone is insufficient unless it is accompanied by strategies that actively cultivate customer engagement.
E-commerce platforms must therefore focus on creating seamless experiences that encourage continuous interaction and emotional involvement, thereby transforming positive experiences into sustained purchasing behavior.
CONCLUSION
This study aimed to examine the influence of customer experience on purchase decisions through the mediating role of customer engagement in digital e-commerce platforms.
By integrating experiential and relational constructs within a management science and information technology framework, this research sought to clarify how digital interactions shape consumer decision making behavior.
The findings demonstrate that customer experience plays a critical role in enhancing both customer engagement and purchase decisions.
A positive digital experience significantly encourages customers to engage more deeply with e-commerce platforms, while also directly influencing their purchasing decisions.
Furthermore, customer engagement was found to be a strong determinant of purchase decisions, confirming its role as a key mechanism that translates experiential value into actual buying behavior.
However, the interaction between customer experience and customer engagement did not significantly affect purchase decisions, indicating that customer engagement functions more effectively as a mediating rather than a moderating variable in the proposed model.
These results contribute to the existing literature by empirically validating an integrated experience engagement purchase framework in a digital e-commerce context.
From a practical perspective, the findings suggest that e-commerce managers should prioritize optimizing digital customer experience by improving system usability, information quality, and service responsiveness, while simultaneously implementing strategies that foster customer engagement, such as interactive features and personalized communication.
Such approaches can enhance customer trust and encourage more consistent purchasing behavior.
Despite its contributions, this study has several limitations.
The use of purposive sampling and crosectional data may limit the generalizability and causal interpretation of the findings.
Future research is encouraged to employ longitudinal designs, probability sampling, and additional variables such as trust or Volume 6 .
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org/10.
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perceived risk to further enrich the model.
In conclusion, this study underscores the strategic importance of customer experience and customer engagement as key drivers of purchase decisions in digital e-commerce environments, offering both theoretical advancement and actionable insights for practitioners and researchers in the digital economy.
ACKNOWLEDGEMENTS
The authors would like to express their sincere gratitude for the financial support provided by the Institute for Research and Community Service (LPPM).
Universitas Muhammadiyah Semarang (UNIMUS), through an internal research grant for the Fiscal Year 2025 (Grant No.
140/UNIMUS.
L/PG/PKLN/PJ.
INT/2.
This support was instrumental in facilitating the completion of this research.
REFERENCES