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As e-commerce evolves, personalized recommendations have become essential for enhancing user experience and website stickiness, yet their impact on customer behavior remains unclear. This study examines how personalized recommendations affect perceived switching costs, focusing on the mediating roles of customer satisfaction and habits, and the moderating effects of consumption levels. Using structural equation modeling on Survey data from 512 Taobao users, findings reveal that personalized recommendations significantly enhance customer satisfaction and habit formation, which fully mediate their impact on perceived switching costs. Additionally, the influence of these mediators varies by consumption level: habits have a stronger effect on perceived switching costs in high-spending groups, while customer satisfaction is more pivotal for low-spending groups. These insights highlight the need for e-commerce platforms to tailor marketing strategies to different customer segments. The study also emphasizes balancing technological innovation, user experience, and consumer rights in the digital marketplace.
Introduction
In today’s rapidly evolving e-commerce environment, personalized recommendation systems have become key tools for enhancing user experience and fostering platform loyalty (Benlian, 2015; Zimmermann et al., 2023). By leveraging user behavior analytics, these systems deliver tailored product suggestions that improve shopping efficiency and customer satisfaction (Xiao & Benbasat, 2007). However, despite their widespread adoption, the psychological mechanisms through which they influence consumer decision-making remain insufficiently understood (Li & Karahanna, 2015; Tam & Ho, 2006). This study addresses a critical gap by investigating how personalized recommendations shape perceived switching costs, defined as the psychological, procedural, and financial burdens customers associate with switching platforms (Burnham et al., 2003). Given that these costs directly determine customer retention (Chen & Hitt, 2002; Pick & Eisend, 2014), elucidating their formation mechanisms is essential for both academic research and business practice.
While acknowledging the broader socio-technical context—including regulatory efforts such as China’s 2022 algorithm regulations emphasizing user rights and transparency (Werner et al., 2022) and ongoing ethical debates around algorithmic fairness (Morley et al., 2020)—our study centers on the psychological foundations of consumer lock-in. Specifically, we examine two key mediating mechanisms: affective evaluation, whereby satisfaction derived from personalized services increases perceived switching costs (Matzler et al., 2015), and behavioral automaticity, whereby habitual usage patterns reinforce platform dependency (Polites & Karahanna, 2012). By integrating these mechanisms into a unified framework, we provide a theoretically grounded account of how algorithmic personalization fosters perceived switching costs, offering actionable implications for both platform design and digital consumer behavior research.
Existing research reveals a fundamental theoretical dichotomy in understanding recommendation systems’ effects. On one hand, satisfaction-oriented studies (e.g., Matzler et al., 2015; Rust & Chung, 2006) demonstrate how affective evaluations elevate perceived switching costs. On the other hand, habit-centric work (e.g., Polites & Karahanna, 2012; Wood & Neal, 2009) establishes cognitive automaticity as an equally potent driver of platform stickiness. Despite their conceptual complementarity, these perspectives have evolved within disciplinary silos, which has led to several critical oversights—most notably, the tendency to examine mechanisms in isolation rather than comparatively, the neglect of potential interactions between satisfaction and habit, and the failure to account for behavioral paradoxes such as habit-sustained loyalty among dissatisfied users. Our dual-process framework transcends this divide by jointly modeling affective and automaticity pathways, thereby offering the first unified account of the psychological mechanisms through which personalized recommendations drive consumer lock-in.
Accordingly, this study investigates three critical questions: (1) How do personalized recommendation systems affect perceived switching costs? (2) How do customer satisfaction and habit formation mediate this relationship? (3) Do these mechanisms operate differently across customer spending levels? While switching intentions and actual switching behaviors represent the ultimate outcomes of interest (Guan et al., 2024; Marx, 2025; Su et al., 2025), our focus on perceived switching costs illuminates the psychological foundations underlying these behavioral responses. By uncovering these perceptual mechanisms, this study offers a crucial foundation for understanding switching behavior in e-commerce contexts and provides actionable insights for both platform optimization strategies and consumer protection frameworks.
This paper is organized as follows: In the second section, we present a review of literature on personalized recommendations, perceived switching costs, customer satisfaction, and habits. This review serves as the foundation for developing our research framework and hypotheses. The third section details our research methodology, including data collection procedures and analytical techniques. In the fourth section, we present our empirical results and findings. Finally, we discuss the implications of our findings, draw conclusions, and identify the study’s theoretical and practical contributions. We also address the limitations of our research and suggest directions for future studies in this field.
Literature review and hypothesis development
Based on the concepts of personalized recommendations, perceived switching costs, customer satisfaction, and habits, this study develops an explanatory model and a set of hypotheses related to shopping websites, as shown in Fig. 1.
[See PDF for image]
Fig. 1
The Conceptual Framework
Personalized recommendations
Personalized recommendation has become an important tool for e-commerce websites to improve customer experience and sales (Benlian, 2015; Zimmermann et al., 2023). Platforms can recommend relevant products for different users’ personalized needs and realize the transformation from general recommendation to precise marketing. For example, when a customer frequently purchases books on Amazon, the platform learns these preferences and suggests similar titles or complementary products like e-readers. Similarly, Netflix analyzes viewing history to recommend content that aligns with user interests, creating a tailored experience. Taobao’s “Guess You Like” feature demonstrates this approach by analyzing users’ search queries, browsing patterns, and purchase history. For example, when a user searches for fitness equipment, the system recommends related items such as sportswear, yoga mats, or protein supplements, thereby creating a personalized experience that both anticipates user needs and introduces relevant products the user may not have initially considered. Many e-commerce platforms have widely adopted personalized recommendation systems to recommend the most interesting products to their target users (Adomavicius et al., 2018). With the rapid development of artificial intelligence (AI) technology, AI-driven personalized recommendation systems are becoming widely applied on retail industry platforms, such as Amazon and Netflix (Du & Xie, 2021). These systems not only enhance user experience and satisfaction but also increase sales revenue and customer loyalty (Benlian, 2015; Kim & Benbasat, 2009).
Grewal et al. (2021) point out that AI improves marketing effectiveness and efficiency through data-driven personalization. Compared to manual work, AI can analyze user data more quickly and comprehensively, thus providing more precise recommendations (Ameen et al., 2021). Furthermore, AI’s capabilities extend to analyzing consumer preferences, automatically curating attractive product combinations, and offering targeted price discounts. This approach enhances the shopping experience while simultaneously improving operational efficiency and reducing costs, creating a mutually beneficial scenario for both consumers and brands. Consequently, marketing initiatives can be tailored based on individual customer preferences for user interface, content, and interaction processes (Zanker et al., 2019).
However, the application of personalized recommendation systems also faces numerous challenges. Li and Karahanna (2015) emphasize that despite the widespread use of these systems, the mechanisms of their impact on customer behavior and decision-making processes are not yet fully understood. Moreover, personalized recommendations also involve ethical issues such as privacy protection, algorithmic bias, and user trust (Morley et al., 2020). In the new regulatory environment, how to balance the commercial value of personalized recommendation systems with ethical responsibility has become a key issue (Floridi & Cowls, 2022). As technology advances and regulations improve, personalized recommendation systems will continue to play a crucial role in e-commerce, but their long-term impact and potential risks require further research and discussion.
Perceived switching costs
Perceived switching costs are the costs that customers perceive they incur when they switch from one provider to another (Burnham et al., 2003). These costs are not only limited to financial expenditures but also include non-monetary factors such as time investment, learning curves, and psychological attachments (Jones et al., 2000). In the context of e-commerce websites, perceived switching costs play a crucial role in influencing consumers’ decisions to stay loyal to a particular platform or to consider migrating to a competitor (Jones et al., 2007; Ping, 1993). High switching costs make it difficult for customers to move to competitors, locking them into engagement with a particular website (Ray et al., 2012). Customer lock-in occurs when perceived switching costs act as a barrier, making it difficult for consumers to switch to a competing platform (Lin et al., 2010). The concept of lock-in is closely related to platform stickiness, or the ability of a platform to retain customers and increase their loyalty (Shih, 2012). Various studies have highlighted the role of switching costs in creating lock-in effects and increasing platform stickiness (Ray et al., 2012). Importantly, perceived switching costs have been established as a robust antecedent to both switching intentions and actual behavior in e-commerce contexts (Guan et al., 2024; Marx, 2025; Su et al., 2025). While the current study examines the formation of these perceptions, prior research confirms their strong predictive validity for retention outcomes. This established relationship justifies our focus on perceived switching costs as the ultimate dependent variable, as it represents the key psychological mechanism through which personalized recommendations ultimately influence customer retention.
Perceived switching costs can be categorized into three main types: procedural, financial, and relational switching costs (Burnham et al., 2003; El-Manstrly, 2016). Procedural switching costs refer to the time and effort required to learn how to use a new platform or adapt to a different user interface. For example, long-time Taobao users who are familiar with its interface, search functions, and cart management may need to invest considerable time in learning JD.com’s distinct navigation system and checkout process. Financial switching costs involve monetary losses or additional expenses associated with switching. These may include membership fees, transaction charges, or the need to repurchase products or services on a new platform. For instance, Taobao users might forfeit unused red packets, accumulated reward points, or membership discounts when transitioning to competitors such as Pinduoduo. Relational switching costs arise from the disruption of established social connections, trusted relationships with sellers or other users, and personalized services offered by the current platform. Taobao users often build long-term relationships with specific sellers, accumulate positive transaction histories, and benefit from tailored customer service—all of which may be lost when switching to an alternative platform. As a result, consumers may hesitate to switch if they anticipate a loss of social capital or personalized experiences.
Customer satisfaction
Customer satisfaction is a key indicator measuring the degree to which products or services meet customer expectations. Oliver and Swan (1989) define customer satisfaction as a consumer’s cognitive evaluation of their experience with a product or service, resulting from a comparison between perceived performance and expectations. This evaluation encompasses not only judgments about the product or service itself but also various aspects related to the transaction. In the e-commerce context, Anderson and Srinivasan (2003) further define e-satisfaction as the contentment of the customer with respect to their prior e-commerce experience.
For e-commerce retailers, the impact of customer satisfaction is multifaceted and far-reaching. First, satisfaction directly influences customer loyalty and repeat purchase behavior. Anderson and Srinivasan (2003) demonstrate a significant positive correlation between e-satisfaction and e-loyalty. Satisfied customers are more likely to continue using the same e-commerce platform and are more willing to recommend the website to others. Secondly, high customer satisfaction can increase website stickiness and usage frequency. Jiang and Rosenbloom (2005) find that customers satisfied with their overall shopping experience tend to increase their visit frequency and time spent on the website, directly translating into more sales opportunities and revenue. Moreover, customer satisfaction influences word-of-mouth for e-commerce retailers. Hennig-Thurau et al. (2004) point out that satisfied customers are more likely to share positive experiences on social media and review platforms, and this electronic word-of-mouth is crucial for attracting new customers and building brand reputation. In the highly competitive e-commerce market, satisfaction can also become a differentiating advantage for businesses. Srinivasan et al. (2002) show that high customer satisfaction can increase customers’ switching costs, making them less likely to switch to competitors’ platforms, thereby building lasting competitive barriers for the business.
In conclusion, customer satisfaction is crucial to the success of e-commerce retailers. It not only affects customers’ repeat purchase behavior and loyalty but also influences website usage frequency, word-of-mouth, and competitive advantage. Oliver and Swan’s (1989) research further emphasizes that satisfaction affects not only direct purchasing behavior but also consumers’ long-term attitudes toward the brand. Therefore, e-commerce retailers should continuously focus on and strive to improve customer satisfaction to achieve long-term success in the competitive online market.
Habits
Habits are automated behavioral patterns that individuals execute in specific situations (Wood & Neal, 2009). In consumer behavior research, habits are described as “learned sequences of acts that become automatic responses to specific situations, which may be functional in obtaining certain goals or end states” (Verplanken et al., 1997, p.540). Responding to the development of information technology, Limayem et al. (2007) further defined habits in the context of information system use as learned sequences of acts that have become automatic responses to specific cues and are functionally defined as behaviors that have become automatic. In the e-commerce environment, habits can manifest as users repeatedly visiting specific websites or using particular applications. For instance, users may develop habits of checking Taobao’s daily deals during lunch breaks or automatically opening food delivery apps at meal times, demonstrating how platform interfaces serve as environmental cues that trigger repetitive behaviors without conscious deliberation.
The formation of habits is a gradual process. According to Lally et al. (2010), forming a new habit takes an average of 66 days, but individual differences are Substantial, and times may range from 18 to 254 days. The key to habit formation lies in the repetition of behavior and the consistency of environmental cues (Wood & Neal, 2009). In the e-commerce context, Hsu et al. (2015) emphasized the importance of satisfaction and perceived usefulness in forming mobile application usage habits. User interface design, personalized recommendation systems, and reward mechanisms can all serve as environmental cues that promote habit formation on e-commerce platforms.
For e-commerce retailers, the formation of user habits has significant implications. Habits increase customer loyalty and continued use (Ashraf et al., 2021; LaRose & Eastin, 2002), while also raising perceived switching costs and reducing competitive threats (Polites & Karahanna, 2012). Additionally, the formation of habits typically leads to a greater regularity in purchasing behavior and a tendency for customers to employ simplified decision heuristics when making choices (Khalifa & Liu, 2007; Murray & Häubl, 2007). In conclusion, understanding and cultivating user habits is crucial for e-commerce retailers. By designing user interfaces and experiences that promote habit formation, retailers can increase customer stickiness and improve their long-term profitability.
Hypothesis development
Personalized recommendations and customer satisfaction
Personalized recommendations have become a cornerstone of modern e-commerce platforms, as these platforms leverage data analytics to tailor product suggestions to individual customer preferences. This customization is designed to enhance the shopping experience by presenting customers with products that closely align with their tastes and needs. For instance, personalized recommendations reduce the cognitive load on customers by simplifying the decision-making process, making it easier for them to find products they are likely to enjoy (Adomavicius & Tuzhilin, 2005). Additionally, personalized recommendations can lead to a sense of discovery and delight as customers are introduced to products they might not have found on their own, further boosting satisfaction (Ansari et al., 2000). These positive experiences not only increase the likelihood of repeat purchases but also foster a stronger emotional connection to the e-commerce platform. Therefore, personalized recommendations are expected to significantly enhance customer satisfaction by providing a more relevant, efficient, and enjoyable shopping experience. Based on the above arguments, we propose the following hypothesis:
H1: Personalized recommendations have a positive influence on customer satisfaction.
Personalized recommendations and habits
In the digital consumption environment, personalized recommendations have become an important tool for enhancing user experience and increasing user loyalty (Benlian, 2015; Tam & Ho, 2006). Based on the analysis of users’ historical behaviors, preferences, and interests, personalized recommendations can provide highly relevant products or content. This customized experience not only meets users’ immediate needs but also gradually reinforces their behavior patterns, leading to the formation of certain habits. Habits can be viewed as automated behavioral tendencies acquired through repetition in stable contexts (Aarts & Dijksterhuis, 2000; Verplanken & Orbell, 2003). When users consistently receive recommendations aligned with their preferences, their repeated interactions (e.g., purchases or clicks) become associated with specific contextual cues (e.g., platform interface or notification triggers), ultimately leading to automatic responses (Aarts & Dijksterhuis, 2000). Personalized recommendations act as an environmental cue that triggers and reinforces habitual behaviors (Verplanken & Wood, 2006). By repeatedly exposing users to contextually consistent choices (e.g., “Recommended for You” sections), platforms create a stable performance environment where behaviors (e.g., clicking recommended items) are repeated without deliberate intent (Verplanken & Orbell, 2022). This aligns with the habit formation process, where repetition in stable contexts reduces reliance on conscious decision-making (De Guinea & Markus, 2009). Personalized recommendations play a key role in driving this process. Based on the above discussion, we propose the following hypothesis:
H2: Personalized recommendations have a positive influence on habits.
Customer satisfaction and habits
Customer satisfaction is widely recognized in consumer behavior research as a key factor influencing consumers’ continued purchasing behavior and brand loyalty (Gustafsson et al., 2005; Oliver, 1999). When customers are satisfied with a product or service, they are more likely to repeat their purchase and continue using the product or service, a process that may gradually evolve into a habit (Aarts et al., 1998). Satisfied customers tend to internalize positive experiences, developing a dependency on the brand or platform, which is further reinforced through repeated behavior (Limayem et al., 2007). Over time, this repeated behavior may become automated in specific contexts, becoming part of a habit (Wood & Neal, 2009). The positive relationship between customer satisfaction and habit can be theoretically explained through the following mechanism: satisfied customers tend to associate their satisfactory experiences with specific products or services, increasing the likelihood of repeat consumption. This repeated behavior not only enhances the quality of the relationship between the customer and the brand but also drives habit formation through continuous positive reinforcement (De Guinea & Markus, 2009). Therefore, we propose the following hypothesis:
H3: Customer satisfaction has a positive influence on habits.
Customer satisfaction and perceived switching costs
Customer satisfaction plays a crucial role in shaping perceived switching costs in the e-commerce environment (Matzler et al., 2015). When consumers are satisfied with the overall service experience provided by a particular online shopping platform, they are more likely to continue using the platform and less inclined to search for or evaluate alternative options (Liu, 2006). Such satisfaction often leads customers to perceive higher potential losses associated with switching service providers, thereby heightening their perception of switching barriers (Jones et al., 2000).
From a procedural perspective, satisfied customers often invest significant time and effort into learning the platform’s interface, navigation processes, and personalized features, leading to the formation of usage habits (Huang & Yu, 1999). This habitual use makes them more likely to repeatedly use the same platform passively rather than actively exploring other options. As users accumulate more purchase history, payment information, delivery addresses, and personal preferences on the platform, the perceived effort required to reconfigure such data elsewhere further elevates procedural switching costs. From a financial perspective, satisfied consumers frequently accumulate various economic benefits on the platform, such as reward points, discount coupons, subscription offers, and exclusive promotions. Switching to another platform may lead to the forfeiture of these benefits, resulting in tangible financial losses (Burnham et al., 2003). Consequently, the greater the satisfaction, the more reluctant consumers become to give up these financial advantages, increasing their perceived financial switching costs (Matzler et al., 2015). From a relational perspective, satisfied customers often develop emotional bonds with the platform and a sense of brand identification. They may also build trust-based relationships with sellers, participate in community engagement, or join membership programs, all of which strengthen their emotional investment in the platform (Pick & Eisend, 2014). According to White and Yanamandram (2004), such customers are more likely to maintain the status quo to avoid the uncertainty and effort involved in establishing new relationships or adapting to unfamiliar platforms, even when those alternatives offer certain advantages.
Therefore, customer satisfaction not only reflects a positive evaluation of current services but also strengthens psychological and behavioral investments in the existing platform. This increases the likelihood that customers will perceive switching to another platform as inconvenient and potentially loss-inducing, thereby elevating their perceived switching costs. Drawing on this theoretical foundation and supporting empirical evidence, we propose the following hypothesis:
H4: Customer satisfaction has a positive influence on perceived switching costs.
Habits and perceived switching costs
Habits play a crucial role in shaping consumer behavior in the e-commerce environment, particularly in influencing perceived switching costs. According to Wood and Neal (2009), habits are automated behavioral patterns that develop through repeated actions in stable contexts. This aligns with Verplanken and Wood’s (2006) argument that habits are triggered by environmental cues and operate independently of conscious intentions, creating a form of cognitive lock-in. In the context of e-commerce, such habits are reflected in users’ routine and repetitive interactions with a specific platform (Limayem et al., 2007). While familiarity (i.e., accumulated knowledge of a platform’s features) may reduce learning costs within a platform, habits extend beyond mere familiarity by embedding automated behavioral patterns that operate with minimal cognitive effort (Verplanken & Wood, 2006). This distinction is crucial because habits not only make interactions more efficient but also create psychological inertia that discourages switching. Over time, these repeated behaviors lead to both behavioral automation and accumulated platform-specific knowledge (Murray & Häubl, 2007). Aarts and Dijksterhuis (2000) emphasize that habits function as automatic knowledge structures that reduce deliberate decision-making, while Verplanken and Orbell (2003) demonstrate how habitual behavior becomes resistant to change through cognitive automaticity.
These habitual responses, once established, not only streamline platform interaction but also generate multiple dimensions of resistance to behavioral change—manifesting as different types of perceived switching costs. From a procedural perspective, habitual users are more likely to experience elevated switching costs, as they have become accustomed to the platform’s layout, functionalities, and interaction patterns. Transitioning to a new platform would require substantial cognitive and behavioral adjustment, reinforcing perceived procedural switching costs (Murray & Häubl, 2007). While psychological attachment may initially be influenced by satisfaction and trust, habitual use reinforces this attachment over time by cultivating a sense of emotional comfort, behavioral consistency, and reduced uncertainty associated with the current platform (Khalifa & Liu, 2007). Environmental cues—such as consistent interface design, recommendation algorithms, and reward mechanisms—further reinforce habitual behaviors and amplify the perceived burden of change (Hsu et al., 2015).
Thus, while familiarity primarily reduces learning costs, habits exert a broader and deeper influence by reinforcing procedural switching costs (due to behavioral routinization), cognitive switching costs (via automatic decision patterns), and psychological switching costs (through emotional comfort and inertia). These effects arise not merely from familiarity, but from the internalization of repetitive behaviors and the resistance to change driven by cognitive automaticity. Therefore, we propose the following hypothesis:
H5: Habits have a positive influence on perceived switching costs.
Moderating effects of monthly spending
Customer satisfaction is widely recognized as a critical factor in increasing perceived switching costs (Matzler et al., 2015). When customers are highly satisfied, they are more likely to perceive greater value in maintaining their current provider relationship, thereby reducing their motivation to explore alternatives (Burnham et al., 2003; Liu, 2006). This satisfaction-driven inertia increases the perceived time, effort, and risk associated with switching (Huang & Yu, 1999; White & Yanamandram, 2004). However, we argue that the strength of the relationship between customer satisfaction and perceived switching costs depends on consumers’ level of monthly spending. Specifically, we propose that monthly spending acts as a moderator that alters the psychological mechanisms through which satisfaction influences perceived switching costs. In this study, high-spending consumers are defined as those who spend more than 2000 RMB per month on the target website, while low-spending consumers spend less than 2000 RMB per month.
When monthly spending is high, consumers face greater financial exposure, which tends to shift their decision-making from affect-driven to risk-sensitive rational evaluation (Fornell, 1992; Keaveney, 1995). These consumers are more likely to assess the cost-effectiveness of their current provider, scrutinize potential alternatives, and discount the emotional value of satisfaction when weighing switching decisions (Pick & Eisend, 2014). As financial risk increases, cost-related considerations begin to outweigh satisfaction-based drivers of loyalty (Dick & Basu, 1994). In contrast, consumers with lower monthly spending experience minimal financial risk, which enables them to rely more on affective evaluations such as satisfaction. In these cases, satisfaction can exert a stronger influence on perceived switching costs, as consumers are less compelled to optimize outcomes based on economic trade-offs (Eggert & Ulaga, 2002). Their decisions are more likely to be guided by inertia and a desire to avoid the cognitive effort associated with change (Anderson & Srinivasan, 2003; Pitta et al., 2006). Based on the above discussion, we propose the following hypothesis:
H6a: The positive effect of customer satisfaction on perceived switching costs is weaker when spending is high than when the spending is low.
Customers’ habits are considered an important factor in increasing perceived switching costs, as habitual behaviors typically form dependence and inertia toward existing services (Polites & Karahanna, 2012). When customers develop a habit of using a particular service, the psychological and operational costs of switching to other services significantly increase, thereby enhancing perceived switching costs (Burnham et al., 2003). This study further investigates whether monthly spending moderates the strength of the relationship between habits and perceived switching costs.
In the high-spending group, consumers have a higher degree of dependence on existing services due to their significant financial and time investments (Anderson & Srinivasan, 2003). This substantial commitment tends to reinforce habitual behaviors, making consumers more reluctant to alter their established patterns of use (Murray & Häubl, 2007). Consequently, consumers in this group are more likely to adhere to their current usage patterns, as switching could entail higher perceived risks and potential losses (Burnham et al., 2003). Conversely, consumers in the low-spending group have comparatively lower financial and psychological investments in existing services. Aarts and Dijksterhuis (2000) note that habits function as automatic knowledge structures, reducing the need for deliberate decision-making. As a result, even if they have established usage habits, they are more likely to consider switching to other service providers (Zeithaml et al., 1996). In low-spending situations, the impact of habits on perceived switching costs may be weaker because consumers perceive lower barriers to switching and are more willing to explore alternatives (Jones et al., 2000). Based on the above discussion, we propose the following hypothesis:
H6b: The positive effect of habits on perceived switching costs is stronger when spending is high than when the spending is low.
Methodology
Measurement development
In developing scales for measurement, we investigated measures in existing literature that could be used for creating the validation scale in our study. Although we did not find complete scales for this study, we identified several useful scales. Most of the scale items were adopted from prior studies in the personalized recommendations and customer satisfaction literature but modified slightly for the online shopping context. The three overall perceived switching costs scales were modified from Ping (1993) and Jones et al. (2000). Customer satisfaction was measured using three items adapted from Oliver and Swan (1989) and Anderson and Srinivasan (2003). Habits were assessed using items adapted from Limayem et al. (2007) and Hsu et al. (2015). The measures for personalized recommendations were modified from Srinivasan et al. (2002) and Steenkamp and Geyskens (2006). The measure of the control variable (i.e., perceived relationship investment) was modified from De Wulf et al. (2001). The respondents scored all items on seven-point Likert scales, with response anchors ranging from strongly disagree (1) to strongly agree (7).
Sampling and data collection procedures
A cross-sectional Survey was used to test the hypothesized relationships in the proposed research model. A pilot study involving 35 college students was conducted from October 21 to 23, 2022, prior to the main data collection phase. The pilot study was used primarily to improve the internal validity of the survey. The specific purpose was to solicit feedback from the subjects regarding the clarity of the survey statements for the purpose of rewording or correcting any statements that were not answered as expected. College students were chosen as the primary subjects for the pilot study because they were accessible, were cooperative, and had experience with online shopping. Participants were asked to read and check the questionnaire for errors or ambiguities. Based on their feedback, minor grammatical errors and ambiguities were corrected. Thereafter, the main data collection was conducted.
For the main data collection, we specifically invited college students who had registered on Taobao before March 1, 2022, to participate in the survey. This criterion ensured that participants had prior experience with the platform before the implementation of the “Internet Information Service Algorithm Recommendation Management Regulations” in China on March 1, 2022. The questionnaire Survey was conducted from October 27 to November 5, 2022, yielding a total of 593 responses. After screening and checking procedures, 81 participants with incomplete responses were excluded. Ultimately, 512 valid questionnaires (valid return rate = 86%) were retained for further data analysis.
College students were selected as participants because they represent a significant demographic of active online shoppers in China and are frequent users of e-commerce platforms like Taobao. While we acknowledge the limitations associated with using a student sample, including potential limited generalizability to broader populations, homogeneity in age and educational background, and possible differences in online shopping behaviors compared to working adults, this demographic is highly relevant to our research objectives, as young adults tend to be early adopters of digital technologies (Rogers, 2003) and are particularly sensitive to algorithmic recommendation systems in their online shopping experiences (McKee, et al., 2024).
Sample characteristics
Table 1 presents the profile of the respondents. Most of the respondents were female (64.06%). Most respondents reported a monthly spending of RMB 1501–2000 (40.82%), while 31.64% reported spending RMB 2001 and above. In addition, most respondents (22.46%) spent 1–2 h per week browsing the website, followed by less than 1 h (22.27%), more than 4 h (20.90%), 2–3 h (19.53%), and 3–4 h (14.84%). Finally, most respondents (40.63%) reported they purchased 4–6 times per month, while 23.24% purchased fewer than 3 times.
Table 1. Summary of respondents’ profile (N = 512)
Variables | Description | Frequency | Percentage |
|---|---|---|---|
Gender | Male | 184 | 35.94% |
Female | 328 | 64.06% | |
Monthly spending | Less than RMB 1000 | 38 | 7.42% |
RMB 1001–RMB 1500 | 103 | 20.12% | |
RMB 1501–RMB 2000 | 209 | 40.82% | |
Above RMB 2000 | 162 | 31.64% | |
Weekly browsing time on Taobao websites | Less than 1 h | 114 | 22.27% |
1–2 h | 115 | 22.46% | |
2–3 h | 100 | 19.53% | |
3–4 h | 76 | 14.84% | |
More than 4 h | 107 | 20.90% | |
Monthly number of purchase on Taobao websites | Fewer than 3 | 119 | 23.24% |
4–6 | 208 | 40.63% | |
7–9 | 74 | 14.45% | |
10–12 | 51 | 9.96% | |
More than 13 | 60 | 11.72% |
US$1 = RMB 7.33 as of Nov 1, 2022
Data analysis and results
The data were analyzed using LISREL 8.72 software to test the theoretical model (Jöreskog & Sörbom, 1999) shown in Fig. 1. Anderson and Gerbing’s (1988) two-step structural equation modeling approach was followed. The two steps were confirmatory factor analysis (CFA) to assess the measurement properties of the reflective latent constructs and structural equation model (SEM) analysis to test our research hypotheses.
Measurement model evaluation
The results of the confirmatory factor analysis in this study met conventional criteria, indicating that our model fit the collected data well: χ2 = 228.75 (p < 0.001), χ2/df = 2.43; comparative fit index (CFI) = 0.99; non-normed fit index (NNFI) = 0.98; and root mean square error of approximation (RMSEA) = 0.053. This study also used extended CFA to further evaluate the quality of the measurement model. Specifically, we assessed the item reliability, internal consistency, and discriminant validity for each construct.
Individual item reliability
The reliability of individual items was assessed through examination of the factor loadings. Factor loadings with scores above 0.70 confirm the convergent validity of the measure (Nunnally, 1978). In the current study, all factor loadings had scores above 0.70 (see scale items in Table 2), indicating good reliability of the constructs.
Table 2. Summary of measures
Construct | Measures | Standardized factor loadinga | |
|---|---|---|---|
Perceived switching costs | (1) For me, the costs in time, money, and effort to another retailer are high | 0.83 | |
(1) In general, it would be a hassle to change retailers | 0.88 | ||
(2) It would take a lot of time and effort to switch to another retailer | 0.76 | ||
Sources: Ping (1993) and Jones et al. (2000) | |||
Customer satisfaction | (1) Overall, I am satisfied with [XX] | 0.92 | |
(2) Overall, I am pleased with [XX] | 0.95 | ||
(3) [XX] meets my expectations | 0.89 | ||
Sources: Oliver and Swan (1989) and Anderson and Srinivasan (2003) | |||
Habits | (1) Shopping at the [XX] has become automatic to me | 0.90 | |
(2) Shopping at the [XX] has become natural to me | 0.91 | ||
(3) Shopping at the [XX] is something I do without thinking | 0.84 | ||
Sources: Limayem et al. (2007) and Hsu et al. (2015) | |||
Personalized recommendations | (2) The advertisements and promotions that [XX] sends to me are tailored to my situation | 0.70 | |
(3) [XX] helps me to evaluate offerings that fit my needs | 0.74 | ||
(4) [XX] makes purchase recommendations that match my needs | 0.74 | ||
(5) [XX] creates the feeling of receiving personalized attention | 0.75 | ||
Sources: Srinivasan et al. (2002) and Steenkamp and Geyskens (2006) | |||
Perceived relationship investment | (1) [XX] really cares about keeping regular customers | 0.91 | |
(2) [XX] makes efforts to increase regular customers’ loyalty | 0.91 | ||
(3) [XX] makes various efforts to improve its tie with regular customers | 0.87 | ||
Sources: De Wulf et al. (2001) | |||
aAll factor loadings are significant at p <.001
The term “XX” is used as a substitute for the actual name of the website. In this study, the websites of Taobao are referenced
Internal consistency
We used two measures to evaluate the internal consistency of constructs: composite reliability (CR) and average variance extracted (AVE). The estimates of CR and AVE were greater than 0.60 and 0.50, respectively, which confirmed internal consistency (Bagozzi & Yi, 1988). As shown in Table 3, the CRs and AVEs ranged from 0.82 to 0.94 and 0.54 to 0.85, respectively. Therefore, all constructs exhibited good internal consistency.
Table 3. Correlation matrix and summary statistics
Variables | Correlationsb | ||||
|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |
1. Perceived switching costs | 0.83a | ||||
2. Customer satisfaction | 0.52 (0.07)c | 0.92 | |||
3. Habits | 0.47 (0.10) | 0.53 (0.07) | 0.88 | ||
4. Personalized recommendations | 0.25 (0.06) | 0.42 (0.05) | 0.37 (0.06) | 0.73 | |
5. Perceived relationship investment | 0.42 (0.08) | 0.64 (0.06) | 0.34 (0.07) | 0.39 (0.08) | 0.90 |
Means | 5.29 | 5.01 | 5.29 | 5.47 | 4.73 |
Standard deviations | 1.44 | 1.11 | 1.44 | 1.15 | 1.15 |
Composite reliability | 0.86 | 0.94 | 0.92 | 0.82 | 0.93 |
Average variance extracted | 0.68 | 0.85 | 0.78 | 0.54 | 0.80 |
aFigures on the diagonal are the square roots of the AVE score for each construct
bAll correlations are significantly less than 1.00
cStandard errors
Discriminant validity
We assessed the discriminant validity of the measures using two different methods. First, we determined whether the correlations among the latent variables were significantly less than one (Bagozzi & Yi, 1988). We constructed 95% confidence intervals for each correlation coefficient. Given that none of the confidence intervals of the Φ-values (± two standard errors) included the value of one, this test supported discriminant validity. Second, as shown in Table 3, the diagonal elements, which represent the square roots of the AVE for each construct, exceeded the off-diagonal elements in the corresponding rows and columns. This pattern demonstrates that each construct shares more variance with its indicators than with other constructs in the model, thereby providing evidence of discriminant validity (Fornell & Larcker, 1981).
Structural model evaluation
The path model
Figure 2 presents the findings for the path model of the proposed model. This model meets the conventional standards, suggesting that our model fits well with the collected data: χ2 (112) = 420.02 (p < 0.001), χ2/df = 3.75, CFI = 0.97, NNFI = 0.97, and RMSEA = 0.073.
[See PDF for image]
Fig. 2
Results of the structure model
We found positive and significant main effects of personalized recommendations on customer satisfaction (γ21 = 0.47, p < 0.001) and habits (γ31 = 0.19, p < 0.001), supporting H1 and H2, respectively. The results also indicated a positive and significant main effect of customer satisfaction on habits (β32 = 0.44, p < 0.001), which supports H3. The results similarly support the positive and direct relationship between customer satisfaction and perceived switching costs, as proposed in H4 (β12 = 0.29, p < 0.001). Finally, the results also indicated a positive and significant main effect of habits on perceived switching costs (β13 = 0.27, p < 0.001), which supports H5.
Post hoc test of alternative models
The mediations implied by the hypotheses and the model in Fig. 2 were tested. This model serves as a baseline; we added a direct path from personalized recommendations to perceived switching costs (M1), with χ2-values of 419.62. The difference in χ2-values between the baseline model and M1 () = 0.40, p > 0.53), with one degree of freedom, tests the significance of the added path. As this difference is not significant, we may conclude that the direct path from personalized recommendations to perceived switching costs is insignificant, therefore showing that customer satisfaction and habits fully mediate the effects of personalized recommendations on perceived switching costs.
The decomposition of indirect effects reveals three significant pathways: PR → SAT → PSC (indirect effect = 0.14), PR → HABIT → PSC (indirect effect = 0.05), and PR → SAT → HABIT → PSC (indirect effect = 0.06). Among these, the PR → SAT → PSC pathway accounts for 56% of the total indirect effect, while the two habit-related pathways (PR → HABIT → PSC and PR → SAT → HABIT → PSC) jointly account for 44%. These results underscore that although affective evaluations (satisfaction) play a dominant role, habit formation also substantially contributes to switching cost development over time.
Moderating effects of monthly spending
According to Baron and Kenny (1986), to examine moderating effects, “the levels of the moderator are treated as different groups” (p. 1175). To test H6a and H6b, we applied multiple-group structural equation modeling. This is relevant to the potential moderating role of monthly spending on the main effects of customer satisfaction and habits on perceived switching costs. We split the sample into two groups based on monthly spending. The low-spending group had 350 members, and the high-spending group had 162 members. In our moderation tests, we compared the two models, one in which we constrained all paths in the two groups as equal and one in which we allowed the path between customer satisfaction and perceived switching costs (or habits and perceived switching costs) to vary across groups. The resulting single degree of freedom χ2 test provided a statistical test of moderation.
The fully constrained model yielded χ2(224) = 811.27. Our results indicate that customer satisfaction had no significant effect on perceived switching costs among high-spending consumers (= −0.02, n.s.) but exhibited a significant positive effect among low-spending consumers ( = 0.34, p < 0.001). To statistically assess the moderating effect of spending level, we conducted a chi-square difference test. Supporting H6a, the difference in chi-square value was significant at the 0.05 level (∆χ2 = 24.59, Δdf = 1, p < 0.05). Furthermore, habits had a stronger positive effect on perceived switching costs for high-spending consumers (= 0.37, p < 0.001) compared to low-spending consumers ( = 0.11, p < 0.01). The chi-square difference was also statistically significant (∆χ2 = 9.99, Δdf = 1, p < 0.05), thereby supporting H6b (for complete moderation results, see Table 4).
Table 4. Results of moderating effects
Paths | High spending (n = 162) | Low spending (n = 350) | Hypothesis | ||
|---|---|---|---|---|---|
β coefficients | t-value | β coefficients | t-value | ||
SAT → PSC | − 0.02 | − 0.55 | 0.34*** | 7.86 | H6a (supported)a |
HABIT → PSC | 0.37*** | 5.85 | 0.11** | 2.96 | H6b (supported)b |
Notes: SAT = customer satisfaction, PSC = perceived switching costs
*p < 0.05, **p < 0.01, ***p < 0.001
a∆χ2 = 24.59, ∆df = 1, p < 0.05
b∆χ2 = 9.99, ∆df = 1, p < 0.05
Conclusion
This study investigated the impact of personalized recommendations on perceived switching costs in e-commerce, with a particular focus on the mediating roles of customer satisfaction and habits. The findings revealed that personalized recommendations significantly enhanced both customer satisfaction and habit formation, which in turn positively influenced perceived switching costs. Moreover, customer satisfaction and habits fully mediated the effect of personalized recommendations on perceived switching costs. Notably, the strength of these relationships varies based on customers’ monthly spending levels: habits have a more substantial impact on perceived switching costs for high-spending customers, whereas satisfaction plays a more influential role for low-spending customers. These findings offer new insights into how personalized recommendation systems shape customer behavior and retention, underscoring the importance of considering customer segmentation in the design of personalization strategies. This study not only deepens our understanding of the complex relationships among personalized recommendations, customer satisfaction, habit formation, and perceived switching costs in e-commerce but also provides empirical evidence for e-commerce platforms to develop differentiated marketing strategies.
Theoretical implications
This study offers several significant theoretical contributions to the fields of e-commerce, personalization, and consumer behavior by integrating and extending existing theories, uncovering new mechanisms, and providing insights into the contextual factors influencing personalized recommendations and customer retention. First, this study advances theoretical understanding of how personalized recommendation systems influence perceived switching costs by uncovering two distinct psychological mechanisms—satisfaction-driven and habit-driven pathways—that increase perceived switching costs. Extending prior research that primarily emphasizes the direct effects of personalized recommendations on customer satisfaction and purchase intentions (Li & Karahanna, 2015; Xiao & Benbasat, 2007), this study reveals that customer satisfaction and habits serve as critical mediators linking personalized recommendations to perceived switching costs. Specifically, positive affective evaluations, such as the perceived relevance of recommendations, enhance emotional attachment to the platform and increase relational and procedural switching costs (Matzler et al., 2015). Meanwhile, repeated exposure to personalized recommendations fosters automated usage patterns (Wood & Neal, 2009), resulting in cognitive lock-in that elevates procedural and financial switching costs (Polites & Karahanna, 2012). By integrating affective (satisfaction) and automaticity (habit) processes, this dual-path framework enriches the perceived switching cost theory proposed by Burnham et al. (2003) and provides a new perspective for understanding customer retention in e-commerce contexts.
Second, this study advances mediation theory by revealing a dual-process mechanism that governs the formation of perceived switching costs. Our results show that the affective pathway (PR → SAT → PSC) accounts for 56% of the total effect (β = 0.14), while the automaticity pathway (PR → HABIT → PSC) explains 20% (β = 0.05), confirming the primacy of affective evaluation in the early stages of lock-in. Notably, satisfaction significantly promotes habit formation (β = 0.44, p < 0.001), and the sequential pathway (PR → SAT → HABIT → PSC) explains an additional 24% of the total effect (β = 0.06). These findings offer a new theoretical perspective on how affective responses translate into behavioral inertia. In the initial phase, affective factors (satisfaction) dominate switching cost formation; over time, usage habits gradually emerge as the primary force sustaining platform stickiness. This transition from affective-driven to habit-driven retention helps explain continued engagement even as satisfaction declines, highlighting a temporal shift that underlies the “satisfaction–habit paradox,” wherein persistent usage becomes decoupled from immediate satisfaction evaluations.
Third, this study advances our understanding of perceived switching cost formation by demonstrating how spending levels fundamentally reconfigure the underlying psychological mechanisms. Our analysis reveals a striking divergence in retention pathways: for low-spending users, the satisfaction-driven pathway exerts a stronger influence on perceived switching costs (β = 0.34 for satisfaction vs. β = 0.11 for habits), consistent with Dick and Basu’s (1994) attitude-loyalty paradigm framework. In contrast, for high-spending users, habitual inertia becomes the dominant force (β = 0.37 for habits vs. β = −0.02, n.s. for satisfaction), supporting Murray and Häubl’s (2007) cognitive lock-in theory. These results advance contingency theory in consumer behavior by demonstrating that psychological lock-in processes are not uniform but vary according to users’ economic engagement with the platform. By resolving inconsistencies in prior research and highlighting the moderating role of spending levels, this study provides a comprehensive framework that bridges information systems research on personalization technologies with marketing theories of customer retention. Personalization strategies should be tailored to users’ spending profiles to maximize long-term retention effectiveness.
Fourth, by focusing on perceived switching costs rather than behavioral outcomes, this study provides a granular understanding of the psychological mechanisms that precede switching decisions. This complements prior work on switching behavior by revealing how these behavioral tendencies are cultivated through personalized systems. Prior research has shown that personalized recommendation systems shape consumer decision-making by reinforcing habitual usage and increasing perceived dependency on a platform (Tam & Ho, 2006). Additionally, perceived switching costs have been identified as a key mediating factor between platform features and customer retention (Burnham et al., 2003). By highlighting these underlying mechanisms, our study bridges the gap between research on personalized systems and consumer switching behavior.
In conclusion, by providing for a more nuanced and integrated understanding of personalized recommendations’ impact on customer behavior, this study advances the theoretical discourse in e-commerce and personalization research while also offering valuable insights for practitioners navigating the complex landscape of digital marketing and customer retention.
Managerial implications
The findings of this study provide significant managerial insights for e-commerce platforms and online retailers. First, the dual-path mechanism of perceived switching costs highlights the strategic importance of personalized recommendation systems in both enhancing satisfaction and fostering habits. Consistent with Adomavicius et al. (2018), who noted that personalized recommendations can significantly improve user experience and sales performance, platforms should invest in advanced recommendation algorithms and data analytics to provide accurate, relevant recommendations that drive immediate satisfaction. Simultaneously, managers should design interaction patterns that cultivate habitual engagement through consistent cross-touchpoint personalization, predictable content refresh schedules, and interface designs that reinforce routine engagement. This balanced approach creates resilient retention mechanisms that persist through satisfaction fluctuations. However, in today’s evolving regulatory landscape, companies must carefully balance personalization benefits with privacy considerations, aligning with Werner et al.’s (2022) findings on the importance of user control in digital environments.
Second, building on the dual-path mediation insights, we propose a dynamic intervention framework to optimize switching cost formation over time. Specifically, platforms should deploy a staged personalization strategy aligned with the temporal evolution of satisfaction and habit effects. In the initial phase, resources should focus on maximizing the emotional value of recommendations, rapidly building user satisfaction through highly accurate recommendations and contextual design features, such as personalized “You May Like” tags. In the middle phase, platforms should activate habit formation tools, such as regular shopping reminders and default option settings, while designing transition mechanisms that leverage the positive effect of satisfaction on habit. In the long term, efforts should shift toward automated maintenance strategies—such as subscription services and frictionless payment systems—to strengthen the habit pathway and mitigate the “satisfaction-habit paradox,” where engagement persists despite declining satisfaction. In addition, platforms should adopt a data-driven dynamic adjustment system by developing a dual-path contribution dashboard that monitors the relative strength of the affective (56%) and habit (20%) pathways in real time. When the habit pathway’s contribution exceeds 30%, the system should automatically trigger habit-enhancing interventions. Furthermore, high-spending users should be prioritized for habit-based interventions, such as exclusive fast-access channels, while low-spending users should receive satisfaction-focused stimuli, such as surprise rewards. This dynamic and data-driven personalization strategy ensures that switching cost formation adapts to both customer lifecycle stages and spending profiles, thereby maximizing retention effectiveness.
Overall, the results of this study highlight the importance of personalized strategies while reminding managers to adopt more detailed and responsible approaches in implementing these strategies to gain a sustained competitive advantage in the highly competitive e-commerce market (Grewal et al., 2021). These findings provide valuable guidance for e-commerce practitioners, helping them develop more effective strategies in the increasingly complex digital marketing environment.
Limitations and directions for future research
While this study provides valuable insights into the impact of personalized recommendations on perceived switching costs, several limitations, which also point to promising avenues for future research, should be acknowledged. First, while our study focuses on perceived switching costs as the ultimate dependent variable, future research could extend these findings by examining actual switching behavior or intentions through longitudinal designs tracking customers across platforms. Perceived switching costs serve as a key psychological barrier to switching, making their formation a critical precursor to understanding switching behavior (Guan et al., 2024; Marx, 2025; Su et al., 2025). Another limitation is the reliance on self-reported data, which may introduce response bias. Future studies could incorporate objective data sources, such as transaction records or behavioral analytics, to enhance validity (Bucklin & Sismeiro, 2009). For instance, clickstream data can reveal user interactions with personalized recommendations, while purchase history can offer a more accurate measure of habits and loyalty. Combining such data with surveys would provide a more robust, multi-method approach to understanding the dynamics between personalized recommendations, customer satisfaction, habit formation, and perceived switching costs (Wedel & Kannan, 2016).
Second, the generalizability of our findings may be limited due to the characteristics of our sample and research context. Specifically, this study focused primarily on college students, who tend to exhibit higher levels of digital literacy and greater familiarity with e-commerce platforms. Their shopping behavior may differ significantly from that of older adults, individuals with lower digital literacy, or consumers from other socio-economic backgrounds. As a result, our findings may not fully capture the behavior of the broader consumer population. To address this limitation, future research should consider recruiting more diverse samples across age groups, educational backgrounds, income levels, and levels of digital proficiency. Moreover, future studies could investigate whether demographic factors such as age, education, and digital literacy moderate the effects of personalized recommendations on perceived switching costs. In addition, while our study is situated within the Chinese e-commerce environment, which offers rich insights into algorithmic personalization, cultural and contextual differences may limit its applicability to other regions. Future research could explore how personalized recommendations influence consumer behavior across different e-commerce models (e.g., B2C, C2C, social commerce) and cultural settings (e.g., individualistic vs. collectivistic cultures) (Hofstede, 2001). Examining the role of personalization in emerging markets with varying levels of e-commerce maturity may also provide valuable insights (Yadav & Pavlou, 2014).
Third, while our study examined customer satisfaction and habits as mediators, it did not explore other potential factors such as trust or customer engagement. Future research should consider these variables to gain a deeper understanding of the complex mechanisms underlying personalized recommendations. Trust, for instance, plays a crucial role in online environments and can significantly influence how customers respond to personalized recommendations (Sarkar et al., 2020). Future studies could explore how personalized recommendations affect trust formation and how this, in turn, impacts perceived switching costs. This line of inquiry could produce important insights into the psychological processes that underpin customer reactions to personalization efforts. Similarly, customer engagement could offer additional insights into the effectiveness of personalization strategies (Pansari & Kumar, 2017). Researchers could investigate how personalized recommendations foster customer engagement and how this engagement relates to switching costs. This could provide valuable information on the behavioral aspects of customer retention in personalized e-commerce environments. By investigating these additional mediating mechanisms, future research could provide a more nuanced and comprehensive model of how personalized recommendations influence customer behavior and retention in e-commerce contexts. This expanded model could potentially reveal new pathways through which personalization systems impact perceived switching costs, thereby enhancing our theoretical understanding and providing more robust guidance for practitioners.
Acknowledgements
We thank the Editor and the anonymous referees for helpful comments and suggestions that greatly improved the paper. Li Xu acknowledges financial support from the Soft Science Research Project of Zhejiang Province in 2024 (2024C35048). Yuan-Teng Hsu is grateful for the project funded by the China Postdoctoral Science Foundation (2023M732269), the project funded by Shanghai Jiao Tong University (2024QN004), and the Shanghai Oriental Talent Program (Youth Project).
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethical approval
This article does not contain any studies with human participants performed by any of the authors.
Competing interests
The authors declare no competing interests.
Adam Vrechopoulos
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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