Determinant of online shopping intention: Satisfaction as an intermediary

Conducted with a sample size of 120, the research employed structural equation modeling for analysis. The findings revealed several influential factors, including perceived trust, perceived value, perceived risk, and satisfaction, impacting behavioral intentions among Indonesian online shoppers. Notably, satisfaction emerged as a mediating variable in the relationship between perceived trust, perceived value, perceived risk, and behavioral intentions. The implications suggest that businesses and online retailers seeking to enhance customer satisfaction and drive desired behaviors should consider strategies aligned with these influential factors. However, it is crucial for both researchers and practitioners to acknowledge the study's limitations. Further research endeavors are encouraged to broaden and refine our understanding of consumer behavior in the Indonesian online shopping landscape.


Introduction
The internet offers constant accessibility to a plethora of products, facilitating seamless transactions.Consequently, consumers face a multitude of options, necessitating a judicious approach in selecting shopping avenues.The dichotomy between online and offline shopping is influenced by various factors, as elucidated by Alam (2008).Parameters such as website design, reliability, customer service, and trust play pivotal roles in shaping online purchasing decisions.Similarly, Assidiqi's (2009) research underscores the significance of usability, interactivity, trustworthiness, aesthetics, and the marketing mix, with usability and aesthetics wielding substantial influence over online purchases of electronic goods.
The wealth of information available online makes internet membership highly advantageous, accessible throughout the day (Engriani & Novaris, 2019).This trend is on the rise in Indonesia (Engriani & Novaris, 2019).Online shopping memberships empower consumers with the flexibility to shop at their convenience, coupled with the added benefits of effortless product research and price comparisons (Al-Debei, Akroush, & Ashouri, 2015).Further insights on traditional information accessed online for technical communication can be gleaned from Al-Debei, Akroush, and Ashouri (2015).Regardless, the online system operates through the internet, utilizing e-commerce platforms, as elucidated by Widyastuti, Hartini, and Artanti (2020).Online purchases are not only free but also accessible 24/7 (Widyastuti, Hartini, & Artanti, 2020).The advent of the internet in Indonesia in the late 1990s facilitated widespread connectivity and created a thriving market.Lasalle's (2011) data supports the continued growth of Business-to-Customer (B2C) E-commerce in Indonesia.Baidoun and Salem's (2023) study delves into the dynamics of the internet, emphasizing its ease of use and connectivity.The contents of websites contribute to online mileage generation while functioning through the internet.This number, available at 357 seconds, can be installed only with the PLS-SEM model, with 311 valid installations.Many individuals accessing the internet and affiliated sites experience positivity, although there is a dearth of available data.Conversely, risk perception exerts a weaker impact.This study underscores the imperative of nurturing trust to surmount obstacles in online shopping.In the COVID-19 era, individuals seek guidance on navigating the online generative network, with a strategic al., 2020) also underscore trust's significance in influencing customer decisions in Internet shopping.
Trust is acknowledged as a fundamental prerequisite for the success of e-commerce (Hoffman et al., 1999), with mobile commerce studies (Dogbe et al., 2019;El-Ebiary et al., 2021) emphasizing trust as the primary factor impacting e-commerce success.Despite recognizing the importance of both price and trust in online stores, there is a need for further exploration of the combined effect of these two factors on online purchase decisions.Internet vendors face a critical strategic decision, contemplating whether to compete primarily based on price or trust, given the ease with which customers can compare prices across different vendors.

Perceived Value
Perceived value, a pivotal outcome of marketing endeavors and a significant element in relationship marketing (Oh, 2003), has recently become a focal point for in-depth study among marketing researchers.Two primary perspectives exist in conceptualizing perceived value.The first approach considers it a construct consisting of two components: the benefits received (economic, social, and relational) and the sacrifices made (price, time, effort, risk, and convenience) by the customer (Dodds, Monroe, & Grewal, 1991;Oh, 2003).
Conversely, a more contemporary perspective views perceived value as a multidimensional construct, gaining popularity in recent research (Al-Jundi et al., 2019;Hernandez-Fernandez, 2019).This viewpoint addresses some limitations of the traditional approach, particularly its excessive focus on economic utility (Mergoni et al., 2022).Furthermore, the multidimensional approach aligns with new theoretical developments in consumer behavior that underscore the influence of emotions in purchasing and consumption habits.
Within the realm of tourism research, perceived value has garnered substantial attention.Some studies adopt the first approach (Shah et al., 2020), while others take a broader view, identifying five dimensions: quality, emotional response, monetary price, behavioral price, and reputation (Gadeikienė & Švarcaitė, 2021).Additional researchers such as Kim et al. (2021) and Kim & So (2023) consider brand, price, and risk as precursors to value formation in the restaurant industry.They find that satisfaction with leisure services is influenced by cognitive and affective evaluations, with the latter being more dominant.

Perceived Risk
Various past researchers have delved into the significance of attitudes toward adopting new technology and novel shopping environments, and empirical evidence substantiates the impact of these attitudes on consumer decisions (Gupta & Mukherjee, 2022).Additionally, personality emerges as a critical factor in the acceptance of technology-driven shopping environments, as individual differences significantly influence customer choices.Personal innovativeness and individual playfulness, for example, have been identified as factors significantly affecting the intention to use new technology and online shopping channels (Palash et al., 2022).Another crucial aspect is perceived risk, given that online shopping often involves higher levels of uncertainty compared to traditional brick-and-mortar stores (Chiu et al., 2009).Greater perceived risk tends to be associated with lower levels of trust (Liao et al., 2011).
This study adopts an integrated model based on personality traits, perceived risk, and technology acceptance to scrutinize their influences on online shopping behavior.Employing meta-analysis, a widely used statistical method for collecting and analyzing results from previous studies to synthesize findings, this approach was applied to determine the average effect size of independent variables on the dependent variable, following the methodology of Paul and Barari (2022) and Khanna et al., (2021).Given its relatively underutilized status in the context of electronic commerce, this study aims to bridge this gap in the existing literature.
According to Paul and Barari (2022), meta-analysis provides more comprehensive insights compared to individual studies by combining results from various settings, helping to average out errors present in each individual work.Many previous studies on online shopping behavior have presented fragmented constructs without integrating them into a cohesive framework.In this study, the researcher aims to address conflicting empirical findings that have emerged over the last two decades concerning online shopping behavior (Floetgen et al., 2021;Sabatini et al., 2023;Scheidt et al., 2020).Therefore, the objective of this study is to test these mediation effects based on the findings of the meta-analysis conducted.

Satisfaction
In this era focused on customer-centric practices, businesses universally aspire to attain customer satisfaction, recognizing it as essential for sustainable growth and a competitive advantage (Deng, Lu, Wei, & Zhang, 2010;Udo, Bagchi, & Kirs, 2010).Customer satisfaction holds particular significance in service recovery and directly influences customers' attitudes and intentions (Holloway, Wang, & Parish, 2005).Post-recovery satisfaction specifically refers to customers' overall contentment with the secondary service (remedial action) provided by a service provider after a service failure.It is crucial to distinguish postrecovery satisfaction from customers' satisfaction with their initial service encounter, also known as secondary satisfaction (Liao et al., 2022).Post-recovery satisfaction has been extensively utilized to gauge the perceived level of justice in various studies (Liao et al., 2022;Mohd-Any et al., 2019;Nguyen, 2021;Schoefer, 2008).
In a survey examining service recovery in the banking sector, Nguyen et al. (2021) found that customers' post-recovery satisfaction increases with perceived distributive justice and interactional justice.This discovery aligns with the study by Mohd-Any et al. (2019) on service recovery practices in different cultural contexts, where distributive justice and procedural justice were observed to enhance post-recovery satisfaction when examining online shopping for electronic devices.Similarly, Phan et al. (2021) proposed that distributive justice, procedural justice, and interactional justice collectively contribute to increasing post-recovery satisfaction

Behavior Intention
Proponents like Kursan (2021) suggest that external factors, such as perceived social pressure, can influence behavior.Previous studies on subjective norms have covered diverse topics, including family Takaful scheme intention, older age infused soft drinks, participation in online communities, and online shopping.These studies often focused on university students as respondents, with some including the general public and professionals.
Contrary to the belief that there is a direct and significant relationship between subjective norms and consumer behavior, personal considerations often outweigh the influence of subjective norms (Jain, 2020).However, studies show that subjective norms indirectly affect consumer behavior through the mediation of purchase intentions (Al Zubaidi, 2020).The influence of families, friends, and the media on actual internet purchasing behavior is minor, with subjective norms identified as the second most influential factor, after perceived behavioral control, in influencing the intention to shop online (Kyle, 2020).The cultural-bound nature of Malaysians, as observed in the study by Kok & Low (2019), contributes to their aversion to changes.
Perceived usefulness, defined as the belief that online websites can provide value and efficacy, influences consumers' online shopping decisions.It is based on the belief that using a system will improve task performance and depends on technological features and personalized services offered by service providers (Driediger & Bhatiasevi, 2019;Grover et al., 2019).Previous studies in developed countries, such as Taiwan, have explored the correlation between perceived usefulness and consumer behaviors (Liao et al., 2011).The perspectives of respondents from developed and developing countries may vary regarding the influence of perceived usefulness on their internet shopping behavior (Peña-García et al., 2020).
In developed countries, factors like price, quality, and durability are prioritized in buying decisions, while considerations in developing countries may differ (Aftab et al., 2023).A study in Malaysia found that the perceived usefulness of a specific system significantly impacted its information system usage, emphasizing consumers' expectation for useful information and a convenient browsing experience when making purchases (Kim & Song, 2010).In summary, perceived usefulness significantly influences consumers' intention to purchase, especially in high-risk situations.

Research Method
The research adopts an explanatory survey approach, aiming not only to describe empirical facts in the field but also to provide an analysis of influences, as explained by Abbas et al. (2020).The unit of analysis comprises all internet users, with a total of 120 people selected as respondents through total sampling.The questionnaire presents statements representing variable indicators, and data collection techniques include field studies, literature reviews, and observational studies following Arboretti et al. (2022).
The stages of questionnaire administration to respondents include: (1) Determining respondents based on criteria, (2) Meeting with predetermined criteria respondents, (3) Respondents filling out the researcher-provided questionnaire, and (4) The completed questionnaires being processed by the researcher.Reliability, a measure of the consistency of measurement results, is assessed according to Siregar (2013) and Clark and Watson (2019).Ghozali (2012) notes that research instruments are considered reliable if the Cronbach's alpha value exceeds 0.70.
For data analysis, the Structural Equation Modeling Partial Least Squares (SEM PLS) method is employed.Validity and reliability analysis involves Confirmatory Factor Analysis (CFA) for construct validity and Cronbach's alpha for reliability.Subsequently, a structural model analysis is conducted to test the relationships between variables using SEM PLS.The significance test of model parameters employs a bootstrap resampling method with a significance level of 0.05.To address potential multicollinearity, discriminant validity ensures that measured constructs or variables are distinct from each other.
In essence, the study tests the variables of perceived trust, perceived value, and perceived risk (X) on behavioral intentions (Y), with satisfaction (Z) acting as the mediator variable, as depicted in Figure 1.

Figure 1. Model of Hypothetical Analysis
The proposed research hypotheses based on Figure 1 are as follows: H1: There is an effect of perceived trust (PT), perceived value (PV), and perceived risk (PR) on satisfaction (S).H2: There is an effect of satisfaction (S) on behavioral intention (BI).H3: There is an effect of perceived trust (PT), perceived value (PV), and perceived risk (PR) on behavioral intention (BI).H4: There is an effect of perceived trust (PT), perceived value (PV), and perceived risk (PR) on behavioral intention (BI) through satisfaction (S)

Validity and Reliability Test
The validity and reliability of each statement item from each variable in this study were assessed using the Confirmatory Factor Analysis (CFA) test through IBM SPSS AMOS 23.The results of the CFA test are presented in Table 2. Based on these fit index values, it can be concluded that the CFA model in this study is compatible with the data.Regarding the reliability test results presented in Table 3, Cronbach's Alpha for each variable in this study is greater than 0.7.Therefore, it can be concluded that each variable in this study is reliable based on these values.
To confirm the acceptability of each hypothesis in this study, the p-values obtained from the SEM test must adhere to the established criteria.The relevant evidence is presented in Table 3.The values in bold and on the diagonal lines in Table 3 represent the square root values of the Average Variance Extracted (AVE) values.Table 3 demonstrates that the square root values of the AVE are greater than the correlation values between the variables below them.This observation indicates that the discriminant validity in this study meets the criteria, and it can be concluded that all statement items from each of the variables used in this study are valid.
The hypothesised results of the study are presented in Table 4. Hypothesis 1 suggests that perceived trust, perceived value, and perceived risk significantly impact satisfaction levels, aligning with prior research and emphasizing the pivotal role of trust and value in online transactions.This study's findings are consistent with the work of Baidoun and Salem (2023), Qalati et al. (2021), andTzavlopoulos et al. (2019), all of which focused on the online shopping behavior of Palestinian millennials.Baidoun and Salem (2023) highlighted the importance of trust and value in shaping millennials' intentions to shop online, finding that factors like perceived usefulness and website quality positively influenced online shopping intention, with trust and perceived value reinforcing these effects.Similarly, Tzavlopoulos et al. (2019) explored perceived quality and value, establishing connections between higher quality, greater perceived value, increased satisfaction, and enhanced loyalty, while simultaneously reducing perceived risk.Qalati et al. (2021) also confirmed the significance of trust-related aspects in online shopping, emphasizing the role of trust in reducing perceived risk and increasing customer loyalty.Thus, this study aligns with the cumulative findings of previous research, contributing to a comprehensive understanding of online consumer behavior across diverse contexts (Baidoun & Salem, 2023;Qalati et al., 2021;Tzavlopoulos et al., 2019).Hypothesis 2 posits that satisfaction significantly influences behavioral intentions, and the robust statistical analysis results support this positive relationship.These findings not only affirm the central role of satisfaction in shaping consumers' behavioral intentions but also align with previous research emphasizing its significance.The results indicate a substantial relationship between satisfaction (S) and behavioral intention (BI), consistent with the studies by Deng, Lu, Wei, & Zhang (2010); Udo, Bagchi, & Kirs (2010); and Liao et al. (2022).Satisfaction is recognized as the influential factor in customers' behavioral intentions (BI).These findings also echo the research of Baidoun and Salem (2023), Qalati et al. (2021), andTzavlopoulos et al. (2019) on Palestinian millennial online shopping behavior, where trust, value, and quality were identified as influencing online shopping intentions, with trust strengthening perceived value and reducing perceived risk (Baidoun & Salem, 2023;Tzavlopoulos et al., 2019;Qalati et al., 2021).In summary, both the current study and previous research collectively contribute to the understanding of online consumer behavior (Baidoun & Salem, 2023;Qalati et al., 2021;Tzavlopoulos et al., 2019).
Hypothesis 3 delves into the impact of perceived trust, perceived value, and perceived risk on behavioral intentions, and the results of the statistical analysis affirm that these three factors significantly influence the formation of behavioral intentions.Perceived Trust (PT), Perceived Value (PV), and Perceived Risk (PR) exhibit a significant positive impact on Behavioral Intention (BI).The results of the statistical analysis indicate that these three factors collectively exert a noteworthy influence on the development of Behavioral Intention (BI), as evidenced by a P-value of 0.000.This underscores the significance of comprehending the five indicators constituting Perceived Trust (PT), the six indicators forming Perceived Value (PV), and the four indicators shaping Perceived Risk (PR) in relation to both favorable and unfavorable Behavioral Intention (BI) manifestations, encompassing a total of seven indicators.
The implications of this study underscore the importance of upholding trust and values within the e-commerce community while being vigilant against undesirable outcomes or risks stemming from the promotion of positive behavioral intentions.Notably, online shopping among millennials significantly contributes to consumer behavior in the digital environment.This finding aligns with prior research conducted by Baidoun and Salem (2023), Qalati et al. (2021), andTzavlopoulos et al. (2019), all of whom explored online shopping behavior among Palestinian millennials.Also, Baidoun and Salem (2023) highlight the pivotal role of trust and values in influencing the online shopping intentions of the millennial generation.This observation is consistent with Tzavlopoulos et al.'s (2019) research, which delved into factors such as quality and perceived value, revealing that higher quality leads to increased perceived value, satisfaction, and loyalty.
Hypothesis 4 examines the combined influence of perceived trust, perceived value, perceived risk, and satisfaction on behavioral intentions.The analysis results reveal a significant combined effect, underscoring the importance of comprehending interactions between variables in shaping consumer behavior.This finding aligns with previous studies by Baidoun & Salem (2023), Qalati et al. (2021), andTzavlopoulos et al. (2019), all of whom investigated online shopping behavior among Palestinian millennials.Baidoun and Salem (2023) emphasized the importance of trust and value in influencing millennials' online shopping intentions.This evidence is consistent with Tzavlopoulos et al.'s (2019) study, which explored factors like perceived quality and value, revealing that higher quality led to increased perceived value, satisfaction, and loyalty.Additionally, this study aligns with Qalati et al.'s (2021) exploration of trust factors in online shopping, highlighting trust's role in reducing perceived risk and increasing customer loyalty.
In summary, these findings offer valuable insights into the factors motivating and shaping online consumer behavior.The implications of this research underscore the importance of building and maintaining trust and value in e-commerce environments to enhance satisfaction and, consequently, encourage positive behavioral intentions.By focusing on the context of online shopping among Palestinian millennials, this research contributes to a deeper understanding of consumer trends in the digital environment.Ultimately, these findings serve as a foundation for developing more effective marketing strategies and improving the customer experience in the world of e-commerce.

Conclusions, suggestions and limitations
In summary, this research delves into the intricate landscape of online consumer behavior, with a specific focus on Palestinian millennials.The study unfolds a narrative that emphasizes the pivotal roles of perceived trust, perceived value, perceived risk, and satisfaction in shaping the behavioral intentions of this demographic in the realm of online shopping.The findings underscore a fundamental connection between perceived trust, value, and risk, and the resulting levels of satisfaction experienced by consumers during online transactions.This interplay highlights the nuanced dynamics at play in the online shopping experience and accentuates the significance of factors such as trustworthiness and perceived value.
Furthermore, the research establishes a clear link between satisfaction and subsequent behavioral intentions.It articulates the notion that satisfied consumers are more likely to exhibit positive intentions in their online shopping behaviors.This aligns with established theories emphasizing the pivotal role of customer satisfaction in influencing future actions.The study also delves into the direct impact of perceived trust, value, and risk on behavioral intentions, providing insight into the multifaceted decision-making processes of Palestinian millennials in the digital marketplace.The intricate relationships between these factors suggest that a holistic understanding is essential for predicting and encouraging positive online consumer behavior.Importantly, the research goes beyond individual factors and explores the combined effect of perceived trust, value, risk, and satisfaction on behavioral intentions.This holistic approach emphasizes the interconnectedness of these elements and reinforces the idea that successful e-commerce strategies must consider the collective influence of these factors.Nevertheless, researchers acknowledge certain limitations, particularly in the survey methodology employed in this study.The reliance on survey data introduces the possibility of response bias and selfreporting errors.Subsequent research endeavors could enhance the robustness of these findings by incorporating mixed methods or employing longitudinal studies for validation.Moreover, researchers are encouraged to conduct analogous studies in diverse cultural and geographic settings across various countries.This approach aims to evaluate the generalizability of the findings and discern potential cultural variations in consumer behavior, paving the way for more comprehensive insights in future research.

Table 3 . Correlation between Variables and Discriminant Validity
Significant at the 0.001 level of significance; PT is Perceived Trust, PV is Perceived Value, PR is Perceived Risk, Sat is Satisfaction, BI is Behavior Intention

Table 4 . The result of the hypothesis testing
Significant at the 0.001 level of significance; PT is Perceived Trust, PV is Perceived Value, PR is Perceived Risk, Sat is Satisfaction, BI is Behavior Intention