Encountering Product Information: How Flashes of Insight Improve Your Decisions on E-Commerce Platforms

Abstract

Serendipity-oriented recommendation systems have been widely applied in major e-commerce and social platforms. Platform managers aim to enhance user satisfaction and increase platform sales by creating serendipitous encounters with information. Previous research has shown that the unexpectedness of encountering product information in serendipity-oriented recommendation systems can effectively stimulate positive emotions in customers, resulting in unplanned purchases, such as impulse buying. However, little research has focused on another critical aspect of encountering product information: perceived value. Our study suggests that encountering product information can positively affect the intention to purchase planned products (focal products) based on their perceived value. To explore this, we conducted three experiments and found that: (1) encountering product information positively influences planned product purchase intention (e.g., reduced decision-making time, improved focal product purchase intention), compared to the absence of encountering product information (precision-oriented recommendation systems); (2) this effect is mediated by customer inspiration; and (3) the characteristics of recommendation system strategies can moderate this effect. Specifically, when the strategy features exhibit a low level of explainability, the impact of encountering product information on customer inspiration and purchase intention is more significant than when a high level of explainability is presented.

1. Introduction

With the rapid development of e-commerce, information encountering (IE) in recommendation systems is gradually replacing precision prediction as a marketing strategy fiercely optimized by e-commerce platforms (such as Amazon, Spotify, and Netflix, et al.) [1]. This strategy not only elevates user engagement and satisfaction, thereby boosting turnover, but it also sustains the effective operation of these platforms. Furthermore, it ensures diversity in product offerings and the health of sellers by enhancing the visibility and sales of both long-tail and cold-start products [2]. Through information encountering, e-commerce customers can unexpectedly or without planning gain access to some product information that stimulates their perceived value, which is called encountering product information (EPI) [3,4]. This type of information not only offers customers a wider range of product options, thereby safeguarding them against the “filter bubble” [5], but it also aids in streamlining their purchase decisions by minimizing decision-making challenges and expediting the decision-making process [6].

Imagine a scenario where you are searching for a suitable laptop bag to complement your newly purchased laptop on an online shopping platform. Based on past experiences, you enter the keywords “laptop bag” into a search engine and are presented with hundreds of options by the recommendation system. At this point, you may start experiencing decision paralysis, facing numerous highly homogeneous products with no clear choice [7]. As you continuously scroll through the recommended products, suddenly a serendipitous encounter with product information captures your attention: either a luxury brand Chloé mini handbag (Scenario 1) or a children’s backpack (Scenario 2, see Figure 1). At this moment, your search goal or preference is likely to be influenced, and you might even make a purchase decision unrelated to your initial needs [8]. Specifically, encountering product information may broaden customers’ scope of product information, leading to unexpected recommendations for high-quality products. This may result in unplanned consumption. For example, after encountering the product information in Scenario 1, customers may continue to search for or purchase other items from the luxury brand Chloé store [9]. Alternatively, you might immediately turn to buy the product indicated by the encountering information, abandon or postpone the purchase of the originally searched-for product, such as directly buying the children’s backpack in Scenario 2 [10], and forgoing the purchase of the laptop bag. However, in reality, we found that besides unplanned purchases, encountering product information may also have a positive impact on planned purchases (also known as focal products). For instance, in the example mentioned previously, it is the appearance of encountering product information such as the luxury brand Chloé mini handbag in Scenario 1 or the children’s backpack in Scenario 2 that inspires customers. This enables customers to efficiently and intentionally choose their desired focal product with similar attributes (such as a luxury brand laptop bag or a laptop backpack) from the myriads of redundant recommended products to a certain extent. In essence, encountering product information has an “inductive” effect on customers’ intention to purchase their planned products [11], which is highly significant for the decision-making process of planned product purchases since most planned purchases are made by the partial planner. Generally, they only have limited basic information about the type or brand of the desired product before consumption. Hence, they rely more on the product information obtained during the purchasing process to make decisions and are more susceptible to external influences [12].

Compared to the explicit nature of IE, encountering product information embedded within is more covert and has received little attention. Research on information encountering highlights the importance of unexpectedness, which is often referred to as “serendipity” in the marketing realm. Serendipity is seen as an unexpected event that combines positivity, unpredictability, and a certain degree of randomness [13,14,15,16]. Studies have shown that experiencing serendipity during the shopping process leads to positive evaluations of products, services, and experiences, as well as the formation of new product preferences. Other scholars regard serendipity as a series of experiences brought about by products, services, or experiences that customers accidentally discover without actively searching, akin to a sense of surprise. These experiences have positive characteristics, such as being lucky, positive, and accidental [14,17], and ultimately have a positive effect on customer attitudes and behaviors [7,18]. In other words, customers feel luckier, more satisfied, and more willing to make a purchase.

While serendipitous encounters may be exciting, they do not always lead to positive outcomes. The resulting uncertainty and lack of control can often trigger negative emotions like anger, disappointment, or anxiety [19], ultimately resulting in decreased customer satisfaction and loss [20]. Despite this, researchers have overlooked a critical component of serendipity: perceived value [21]. Rond (2014), in his dissertation “The Structure of Serendipity”, emphasized that failing to understand the perceived value of serendipitous information is like telling only a small, interesting story. To truly grasp serendipity, we must recognize the potential for seemingly unrelated events to combine in a valuable way. This demands a “prepared mind” and an “open mindset” that enable us to recognize the potential value in apparently unrelated encountering information. The strong interaction between opportunity and a prepared mind is the cornerstone of creativity, much like many great scientific inventions have emerged by recognizing the role of perceived value in serendipitous information, such as penicillin, Java programming, or Post-it notes [22]. The scholar who coined the phrase “seeing a bridge where others see a hole” aptly summarized the role of perceived value in serendipitous information. Given this context, our study aims to expand the concept of perceived value from the natural sciences to e-commerce recommendation systems. By revealing hidden connections or analogies between seemingly unrelated events, encountering information can serve as a powerful diagnostic tool for customers. It can aid customers in refining and optimizing their product searches, increasing efficiency, and improving decision-making [6].

Based on the above, this study seeks to explore the impact of encountering product information on customers’ intention to purchase focal products and its underlying mechanisms. To do so, a combination of the accessibility-knowledge framework and customer inspiration theory will be leveraged, leading to three research questions. These questions are:

• Does encountering product information affect customers’ planned purchases (the focal product)?
• What is the underlying mechanism behind the effect of encountering product information on planned purchases (the focal product)?
• What are the boundary conditions of the main effects based on the characteristics of the recommender system strategy, the accessibility-knowledge framework, and customer inspiration?

This study attempted to extend the utilization of encountering product information in the customer behavior domain by integrating the accessibility-knowledge framework with customer inspiration. Through this, it aims to enrich the theoretical framework of customer inspiration, an emerging field of research. In practice, this study provides valuable insights for e-commerce platforms and managers on how to use information-encountering strategies effectively, optimize shopping decisions, improve online product information search patterns, enhance the value of recommendations, and release the potential value of information on time.

2. Literature Review

2.1. Information Encountering (IE) and Encountering Information (EI)

The concept of “Information Encountering” was first introduced by Bernier in 1960, who referred to it as “serendipity” in the field of library and information science. Since then, research on serendipity and its related concepts has grown and diversified. Erdelez, a prominent scholar, introduced the term “information encountering” in the context of information retrieval. Her research has provided various perspectives on the concept of IE, including its context, characteristics, processes, experiences, and utilization. The widely accepted definition of “information encountering” is an unexpected situation in which individuals accidentally acquire information that interests them or meets their needs [21]. Current research on information encountering can be divided into two primary branches: exploring the behavioral processes and triggering factors [23,24] and analyzing the utilization of encountering information, including its use, preservation, and sharing [21]. This study focuses on how individuals adopt and utilize encountering information to make online consumption decisions after IE. In this regard, the specific e-commerce context of encountering product information is taken as the research object to explore the phenomenon.

Encountering information refers to the information obtained by individuals while experiencing IE. Generally, a piece of qualified encountering information is unexpectedly discovered by individuals without anticipation or planning and can trigger their perception of value [25]. It possesses characteristics of unexpectedness, value, and insight [4]. It is well known that many great inventions in natural science are inseparable from encountering information, such as aspirin, penicillin, DNA, and Post-it notes [26]. The role of encountering information in scientific discoveries can be classified into four types: (1) finding something one is actively looking for in an unexpected way (penicillin); (2) unexpectedly discovering a solution for target B while searching for a solution for target A (Post-it notes); (3) accidentally finding something valuable that was not actively sought (PCR tests); (4) unexpectedly combining a large amount of background knowledge to find what one is actively seeking (the discovery of DNA). The fourth type is considered pseudo-encountering information because the luck in scientific discovery is based on scientists purposefully seeking important things, enabling them to recognize the value in accidental discoveries. Therefore, scholars like Rond considered encountering information as an ability, a capability to identify the combination between events. They believed that each of the scientific inventions influenced by encountering information requires inventors to have a “prepared mind” and an “open mindset”, which allows them to immediately recognize the potential value of encountering information after IE. This interaction between opportunity and a prepared mind [26] is the basis for the hypotheses in this study that encountering information can strengthen and induce customers’ planned purchases.

2.2. The Impact of Encountering Product Information on Customers’ Decision-Making

Encountering product information can be seen as a specific type of encountering information in electronic commerce. It encompasses all details related to products or services on platforms, including their prices, brands, attributes, uses, after-sales service, payment, shipping, and more. Typically, this information is not limited to static modes like text and images; it may also involve various interactive forms, such as dynamic images, short videos, and other multimedia content, to guide and facilitate customer decision-making [9,27]. Similar to encountering information in general, encountering product information also possesses unexpectedness, value, and insight. The unexpectedness is mainly reflected in the fact that encountering product information should be independent of direct or significant conceptual associations with the planned purchasing product, for instance, having noticeable differences from the keywords or details of the targeted product. Regarding information categorization, differences should exist between the encountering product information and the product information customers are searching for. Additionally, value and insight are the customers’ abilities to recognize that the information can be used to solve specific problems or fulfill their interest preferences [4].

Previous research has extensively demonstrated that information about irrelevant options significantly impacts decision-making, which can explain why encountering product information, characterized by unexpectedness and substantial differences, is perceived as valuable to specific customers and potentially influences their subsequent consumption behavior (see

Table 1. Literature review of irrelevant options in purchase decision-making.

Source	Effect	Product Types	Findings
Ge, Messinger, and Li (2009) [28]	Information Cascades Effect	Sold-out products	The existence of out-of-stock products within the context of decision-making can motivate customers to purchase the currently available options and ultimately reduce the tendency to delay making a choice. Two underlying decision processes: a sense of urgency and perceived attractiveness of products similar to the sold-out products.
Shocker, Bayus, and Kim (2004); Russell (1999) [29,30]	Cross- Category Effects	“Other Products”	“Other products” can directly or indirectly affect buyer demand for the focal product. The demand for the focal product may also be connected to the demand for products in other categories.
Pettibone and Wedell (2007) [31]	Phantom Decoy Effects	Phantom Decoy	The decoy options can contextually influence customers’ purchasing decisions regarding available options. There are three explanations for this effect: similarity to the phantom, perceived losses and gains, or the relative weighting of dimensions.
Sun and Zhou (2012); Hamilton, Hong	Feature Convergence Effects	Products with similar attributes	Customers are more willing to choose similar products to ensure quality and reduce risk.
Chernev (2007) [11,32]	Perceptual Focus Effects		Due to excessive exposure to similar attributes, people focus on unique attributes.

). An Informational Cascades Effect posits that encountering product information becomes a “reference point” and inspirational cue for customers to evaluate focal product choices, potentially enhancing the attractiveness of focal products perceived similarly to the encountered product [28]. Similarly, Cross-Category Effects have also identified that customers’ choices in one category may be influenced by previous ownership, use, or experience with products in other categories. When considering an anchor product, the activated product features can be applied to the target product and serve as an effective basis for evaluating the target product. For instance, customers satisfied with a specific brand (Bosch) or technology (dual-turbine) in one category (washing machine) may purchase products in other categories (dishwasher) with the same brand or technology, even if these two types of products (washing machine and dishwasher) seem unrelated [29,30]. Additionally, the Phantom Decoy Effects provide a possible explanation for the impact of encountering information on the focal product purchase intention. To some extent, the encountering product can also be viewed as a decoy alternative, highly attractive but unavailable in the choice set. The phantom decoy effects suggest that information about decoy options can contextually influence customers’ purchasing decisions regarding available options. Due to limitations in processing large amounts of information, customers generally do not form definite preferences before purchasing, and their final purchasing decision relies on the selective processing and assimilating of “at-the-time-of-purchase” information. Therefore, adding or removing a product from a recommendation set, even if it is an encountering product, can systematically influence preferences and choices for existing options [31]. As is well known, it is an essential way to make comparisons to product features (cue of context) for customers to obtain access to information and make inferences, so the impact of encountering product information on focal product choices can also be explained through the Effect of Perceptual Focus and Effect of Feature Convergence. According to the Feature Convergence Effect, people are more willing to choose products with similar attributes to ensure product quality and reduce risk. In contrast, the Perceptual Focus Effect suggests that the perceived similarity between the encountering product and the focal product may lead to excessive exposure to similar attributes so that individuals focus more on specific attributes that are different from those of the encountering information and prefer other focal products with those attributes. This process occurs automatically and effortlessly in intuitive processing modes. In this way, whether choosing similar or dissimilar focal products, the appearance of encountering product information may promote planned purchasing, such as the intention to purchase focal products or reduce decision time [11,32].

Based on the above, we propose the following hypothesis 1:

H1: 

Encountering product information has a positive impact on the planned purchase.

2.3. Customer Inspiration

Inspiration is considered an intrinsically motivated state evoked by an external source and is conceptualized by social psychology in terms of three key features: arousal, transcendence, and motivation. Arousal means that inspiration is triggered by the stimulation of external information rather than being spontaneously generated by the individual; transcendence involves a feeling of positivity and self-improvement, which leads people to see better possibilities; inspiration leads to approach motivation, which prompts the individual to express a new perception or understanding of something [33]. Böttger proposed the concept of “customer inspiration” by combining the general conceptualization of inspiration with marketing practice. They pointed out that customer inspiration is a temporary activation state of customers, in which customers are more likely to be guided by marketing, thus prompting them to change from receiving marketing guidance to the internal pursuit of consumption-related goals [34].

The arousal of customer inspiration is generally stimulated by external information combined with the customer’s knowledge, experience, and common sense. First, the characteristics of the sources that are believed to inspire customer inspiration should include:

• containing deeply inspiring content (such as a new combination or new ideas);
• encouraging the use of imagination;
• an inspiring approach to motivation rather than avoidance motivation.

Meanwhile, customer characteristics should manifest as an open mindset and a prepared mind for inspiration. It matches quite well with the characteristics of the source and receiver required for encountering product information to be effective. Since encountering product information is considered to satisfy the notion of not being able to have direct or obvious conceptual associations with the focal shopping task of the customer, that is, in information categorization, encountering product information has differences compared to focal product information, such as presenting children’s backpacks on the laptop bag search page [21]. In this way, by unexpectedly presenting information about products utterly different from the currently searched product, it easily attracts customers with relevant knowledge (open mindset and a prepared mind) due to the inconsistency with background information and may inspire them to spark creativity. For example, they may consider purchasing a laptop bag with a backpack style. It should be emphasized that, according to the framework of knowledge accessibility, since the conditions for the establishment of encountering product information must be satisfied to meet the customer’s interests or to hit the customer’s needs, once the customer recognizes this type of information, it means that the knowledge or experience of this information has long been stored in their memory. This knowledge may impact subsequent behaviors, even under completely different conditions [35]. For instance, a customer with dual roles as a mother and an employee may pay attention to children’s backpacks as a product when acting as a mother. When irrelevant information like children’s backpacks appears on the laptop bag search page (potential encountering product information), the background knowledge or experience of the customer’s mother role (prepared mind) will prompt them to quickly recognize the value of children’s backpacks (confirmed encountering product information) and use it as an anchoring point, generating inspiration for the fusion of product functions, such as having the comfort of a backpack and meeting the functionality of a laptop bag [36]. This process contributes to forming the final purchasing decision [37]. Activated customer inspiration is also accompanied by strong positive emotion (a flash of surprise and joy), jointly influencing consumer decisions, encouraging customers to complete the purchase plan for the focal product actively, reducing purchase delays, and lowering platform conversion rates [34]. In summary, we propose the following hypothesis 2:

H2: 

The positive impact of encountering product information on the planned purchase is mediated by customer inspiration.

2.4. Characteristics of Recommender System Strategy: Degree of Explainability

The previous literature indicates that a key dimension influencing customer response to recommendations is the explainability of recommendations [38]. Explainability refers to the ability of a recommender system to clarify the decision-making process and the legitimacy of the results, i.e., why and how the recommender system works. If a recommended item does not perform as expected or the results are subpar, the explainability of the recommender system can help maintain users’ trust in the system. However, when users come across product information that does not align with their current search, it can be perceived as a recommendation failure, causing cognitive dissonance [37]. To avoid this, platforms often implement strategies to reduce unexplainability and improve transparency in recommendations. For example, when product information is encountered, platforms may explicitly inform customers as to why they are seeing that particular information in that context, using prompts like “recommended based on your browsing history” or “similar friends have purchased”.

While reducing uninterpretability can effectively explain the operations behind the algorithm’s black box, promote algorithm transparency, and alleviate customer distrust [38], it may hinder the effectiveness of encountering product information. Highlighting transparency can lead customers to attribute the impact to marketing personnel rather than tracing it back to the clues, which undoubtedly hinders the activation of consumer inspiration [14]. For instance, when searching for a specific product (e.g., a laptop bag), comparing the recommender system presents encountering product information (e.g., children’s backpack) without hints (low explainability), the presence of hints in encountering product information (high explainability) such as “because you have previously browsed this children’s clothing brand” may reduce customers’ understanding and reflection on encountering product information with inconsistent characteristics, suppressing customer imagination and creativity [39]. Therefore, this study predicts that, compared to attributing it to marketing personnel (high explainability), customers believing that encountering product information is a random recommendation result (low explainability) will significantly enhance the promotional effect of encountering product information on customer inspiration. Additionally, since novel knowledge and information are key factors affecting the generation of customer inspiration, the discrepancy between encountering the product and the focal product will stimulate customers to speculate and imagine their intrinsic connections, expanding customers’ horizons, promoting the generation of new ideas and understandings, which will lead to the generation of specific brands and products converging to the fusion of the two, and effectively enhance the propensity to purchase the focal product [33]. In summary, we propose hypothesis 3 (Figure 2):

H3: 

The characteristics of the recommender system strategy: the degree of explainability plays a moderating role in the relationship between encountering product information and customer inspiration, as well as the relationship between encountering product information and planned purchase. Specifically, when compared to a highly explainable strategy, adopting a low degree of explainability amplifies the positive impact of encountering product information on customer inspiration and planned purchase.

3. Research Methodology and Data Analysis Results

To explore the hypotheses presented, we conducted three studies to investigate how encountering product information impacts the planned purchased. In order to examine the robustness of the model, we used different variables to verify the effects, as both of them can evaluate planned purchases from various aspects. Our first study involved setting up a realistic purchase scenario to confirm the main effect of encountering product information on focal product purchase intention. The second study used Camtasia Studio software (version 2021.0.15), a software application that can record users’ page browsing behavior, to track decision-making time to validate the mediating role of customer inspiration and eliminate the possibility of novelty as an alternative explanation. Our third study focused on examining the moderator of explainability on the effect of EPI on focal product purchase intention, which is summarized in

Table 2. Research Summary.

	Research Purpose	Research Design
Study 1	Verify whether encountering product information affects focal product purchase intention (H1)	Between-groups experiment; 2 (encountering product information: presented vs. not presented) × 1 (focal product purchase intention)
Study 2	Verify the mediating role of customer inspiration (H2) and rule out alternative explanation (novelty)	Between-groups experiment; 2 (encountering product information: presented vs. not presented) × 1 (time of decision-making)
Study 3	Examined explainability as a moderator in the effect of encountering product information on focal product purchase intention (H3)	Between-groups experiment; 2 (condition: IE vs. accuracy) × 2 (explainability: high vs. low)

.

3.1. Study 1: Verify the Main Effect of Encountering Product Information (EPI) on Intention to Purchase Focal Product

3.1.1. Participants and Design

The objective of Study 1 was to validate the main effect. To achieve this, 100 undergraduates were recruited from a university in central China. However, two participants were eliminated from the study as they had accessed or purchased products that were comparable to the ones being tested, resulting in a reliable sample of 98 (M_age = 21.5, female: 67%). The study adopted a one-way (encountering product information: presented vs. not presented) between-groups design.

Since the encountering product information needs to satisfy the customers’ interest or perception of value, we adopted product information from the e-commerce customers’ historical browsing records or purchasing records as the experimental encountering product information to attract customers’ attention. The experimental process proceeded as follows: one day before the start of the formal experiment, participants were asked to submit a piece of product information from their purchase history or browsing history on the e-commerce shopping platform within the last seven days. This information would be used in the study as the product information encountered by the participants. The formal experiment took place the next day, 15 min before an English class. To prevent participants from guessing the purpose of the experiment, the English teacher first asked them to consider purchasing an English learning machine to fulfill the requirements for listening and speaking in subsequent learning stages. Then, participants were directed to browse for an English learning machine on an e-commerce platform similar to Taobao’s page (a well-known e-commerce platform in China) as predetermined by the experimenter. Participants were informed that they could make an immediate purchase or buy it from other platforms after the class. While browsing for products, participants in the EIP condition received recommendations for learning machines as well as recommendations for products they had either consumed or browsed the previous day; in the condition without EIP, participants only received recommendations for learning machines. After about 15 min, we measured the perception of encountering product information with the following questions: “I feel that some recommended products are unexpected”, “interesting”, “I am inspired by some recommended products”, and “I am surprised” (1 = completely disagree, 7 = completely agree). The mean of the scores for the four items was used as a basis for the manipulation test of the presentation of encountering product information. Afterward, due to the high correlation between purchase delay and purchase intention, this experiment measured both purchase delay and purchase intention to confirm the hypotheses from multiple perspectives. Participants were asked, “I would buy the English learning machine on this platform immediately” and “I would like to buy the English learning machine on this platform” (1 = very unlikely; 7 = very likely). Finally, each participant filled in personal information such as their gender, age, and monthly disposable income (

Table 3. Variable measurement.

Variables	Measurement	Cronbach’s α
Encountering product information perception	I feel that some recommended products are unexpected. I feel that some recommended products are interesting. I am inspired by some recommended products. I am surprised by some recommended products.	0.777
Focal product Purchase intention	I would buy the English learning machine on this platform immediately. I would like to buy the English learning machine on this platform.	0.657
Customer inspiration	I was stimulated to imagine something. I got a new idea unexpectedly and spontaneously. My mind was broadened. Aha, it looks like it could be like this!	0.878
Novelty	I found the list of recommendations to be new. I found the list of recommendations to be fresh. I found the list of recommendations to be novel.	0.827
The time of decision-making	Collected from Camtasia Studio.
Explainability	I understand why this product was recommended to me. This cue explains the system’s intention of recommending it to me well. I learned about the mechanism of recommender system.	0.769

).

3.1.2. Results

Manipulation check. Participants in the EPI-presented condition (M = 5.04, SD = 1.08) indicated greater perceived serendipity than those in the non-presented condition (M = 4.39, SD = 1.25; F (1,98) = 7.32, p < 0.01). Therefore, the manipulation of EPI was successful.

Main effect. The results of a one-way ANOVA showed that there was a significant main effect of encountering product information on the intention to purchase the focal product (F (1,96) = 35.37, p < 0.001). When customers were presented with EPI (M = 5.34, SD = 1.09), their intention to purchase the focal product was greater compared to when EPI was not presented (M = 4.05, SD = 1.05).

3.1.3. Discussion

In Study 1, a realistic purchasing scenario was simulated by utilizing product information from the browsing or purchase history within a 7-day timeframe. The findings indicated that this approach increased the likelihood of purchasing the focal product while also reducing any potential delay in the decision-making process.

3.2. Study 2: The Mediator Role of Customer Inspiration

3.2.1. Participants and Design

The primary purpose of Study 2 was to verify the mediating role of customer inspiration and rule out the alternative explanation of the role of novelty. Relevant research has shown that when products with significant differences, like fusion products and cross-border products, are presented simultaneously, customers tend to perceive novelty, which can affect their purchase decisions and evaluations [40]. As such, we included the perception of novelty as a control variable to further verify the role of customer inspiration.

According to the research by Willemsen et al., providing customers with a recommendation list that exceeds 30 items can lead to confusion and issues such as delayed consumption and switching platforms [41]. It is common for customers to feel overwhelmed when faced with too many choices while shopping online, particularly when they lack relevant knowledge for planned or partially planned purchases [12]. Rather than inundating customers with more options, excessively precise recommendations can cause cognitive strain and negative emotions, potentially causing customers to abandon their planned purchases. In order to simulate the experience of daily e-commerce consumption, we selected 26 relevant recommendations from a platform website and interspersed four encountering products from other categories as simulated recommended webpage content (two pages) for customers to choose from. This study was conducted in one-way (encountering product information: presented vs. not presented) between-groups design, with 54 undergraduate and graduate students (M_age = 24.23, female = 47.4%) from a medical university in central China participating in the experiment in return for a small monetary reward.

At first, participants were provided with an overview of the simulated recommendation webpage and its usage. After that, they were asked to find out a hotpot restaurant to dine with friends. Despite their familiarity with hotpot restaurants near their university, we intentionally recommended restaurants located 30 km away from their university. Participants in the EPI condition had access to 26 hotpot restaurants and four other restaurants, including two barbecue restaurants and two milk-tea shops. Meanwhile, those not in the EPI condition could only browse 30 hotpot restaurants. Their browsing and searching behavior, decision-making time, and the entire process of browsing and deciding were recorded using Camtasia Studio. We selected their decision-making time, a representative indicator for decision-making delays (decision efficiency), to measure their intention to purchase the planned product. After selecting a restaurant, participants completed the same measurement items as in Study 1 for EPI manipulation, followed by measurements of customer inspiration (“I was stimulated to imagine something”; “I got a new idea unexpectedly and spontaneously”; “My mind was broadened”; “Aha, it looks like it could be like this!”) and novelty (“I found the list of recommendations to be new/fresh/novel”). Finally, participants provided personal information such as gender, age, and disposable personal income (Table 3).

3.2.2. Results

Manipulation check. The results of the one-way ANOVA indicated that there was a significant difference between the two EPI conditions (F (1,55) = 118.47, p < 0.001). Participants reported feeling greater serendipity in the condition where EPI was presented (M = 5.77, SD = 0.76) compared to the condition where EPI was not presented (M = 3.13; SD = 1.03). Therefore, the manipulation of EPI was effective.

The effect of EPI on decision-making delays. Using Camtasia Studio software, we gathered data on participants’ browsing and decision-making times to investigate the impact of EPI on purchase delays. One-way ANOVA revealed a significant main effect of encountering product information on the purchase delay of the focal product (F (1,55) = 191.95, p < 0.01). When EPI was presented, the decision-making time was significantly lower (M = 130.52 s, SD = 10.28) compared to when EPI was not presented (M = 278.50 s, SD = 54.58). Therefore, hypothesis 1 has been verified.

The mediator of customer inspiration. Taking customer inspiration as the dependent variable, one-way ANOVA revealed that participants in the condition of EPI presented [M = 5.58, SD = 0.52] perceived more inspiration than the condition of EPI not presented [M = 3.23, SD = 0.95; F (1,55) = 128.87, p < 0.001]. We conducted bootstrapping mediation analyses (PROCESS Model 4) using condition (EPI presented vs. EPI not presented) as the independent variable and customer inspiration as the mediator for decision-making delays. The procedure used bias-corrected bootstrapping to generate 5000 resamples, resulting in a 95% confidence interval. In this mediation model, the results indicated that customer inspiration has a significantly negative indirect effect on decision-making delays (β = −13.94, 95% CI = [−29.22, −1.25]).

Novelty. Taking novelty as the dependent variable, one-way ANOVA revealed that participants in the condition of EPI presented (M = 5.74, SD = 0.64) significantly perceived novelty compared to the condition of EPI not presented (M = 3.88, SD = 1.49; F (1,55) = 35.19, p < 0.001). However, through mediation analyses PROCESS 4, the indirect effect of novelty was insignificant for decision-making delays (β = 2.49, 95% CI = [−6.81, 10.71]). Therefore, novelty cannot be considered as an alternative explanation.

3.2.3. Discussion

By employing different scenarios, Study 2 revealed that customers who were exposed to encountering product information could significantly reduce their decision-making time and delay in subsequent consumption decisions, thereby supporting the primary conclusion. This study also confirmed that customer inspiration plays a crucial role in the process. In addition, Study 2 effectively ruled out novelty as a potential alternative explanation and established that although encountering product information may lead to a sense of novelty, it does not serve as a mediator for its impact on customers’ subsequent decisions.

3.3. Study 3: Characteristics of Recommender System Strategy: Degree of Explainability

3.3.1. Participants and Design

Study 3 investigated the degree of explainability in moderating customer inspiration and its outcome. We proposed that a recommendation strategy with low explainability would enhance customers’ speculation and understanding of the connection between the information about the encountering product and the focal product, eventually leading to increased inspiration. This study employed a between-subjects design of 2 (condition: IE vs. accuracy × explainability: low vs. high). A total of 176 participants from a university in central China were randomly assigned to the groups but five subjects were excluded due to failing an attention test. Finally, we obtained data from 171 valid participants (M_age = 19.25, female: 52%).

The procedure used in Study 2 closely resembles that of Study 1, with the addition of a description of the recommender system, its operation, and manipulation measures. In the IE condition, participants would be presented with a cue below the product shown, see Figure 3: A1 “Recommended for you” (low explainability); A2 “Recommended for you based on your browsing history” (high explainability). In the accuracy condition, the system would randomly select the recommended items information below the prompt: B1 “Recommended for you” (low explainability); B2 “According to your browsing history, recommended for you” (high explainability). The manipulation measures for the characteristics of the recommendation system strategy included the following items: “I understand why this product was recommended to me”, “This cue explains the system’s intention of recommending it to me well”, and “I learned about the mechanism of the recommender system”. As in Study 1, the perceptions of encountering product information, as well as the measures of purchase delay and purchase intention, remained nearly the same, with some modifications to the descriptions. In this study, to verify the stability of the mediation effect, customer inspiration was measured again in this context, and the measurement items were the same as in Study 2.

3.3.2. Results

Manipulation check. Results from one-way ANOVA showed that participants in the IE condition (M = 5.30, SD = 1.58) perceived greater serendipity than did those in the accuracy condition (M = 3.31, SD = 1.11; F(1,169) = 92.75, p < 0.01). Additionally, participants in the high-explainability condition (M = 5.51, SD = 1.16) reported recommendations as more explainable compared to those in the low-explainability condition (M = 3.73, SD = 1.12; F (1,169) = 103.28, p < 0.001). Therefore, we can conclude that the IE and explainability manipulations were effective.

Main effect. A 2 (condition) × 2 (explainability) ANOVA revealed a main effect of explainability, such that the purchase intention of the focal product was higher when the explainability was low (M = 5.04, SD = 0.07) rather than high (M = 3.57, SD = 0.7; F (1,167) = 183.30, p < 0.001). Moreover, there was an effect of information encountering (F (1,167) = 60.04, p < 0.001), such that the purchase intention of the focal product was higher when the recommended item was randomly (M = 4.73, SD = 0.08) rather than deterministically (M = 3.88, SD = 0.07) encountered. The interaction was significant (F(1,167) = 160.17, p < 0.001) as well. Specifically, when there was a low degree of explainability, the purchase intention of the focal product was much greater in the condition of IE (M = 6.15, SD = 0.62) than in the accuracy condition (M = 3.93, SD = 0.93; F (1,167) =106.11, p < 0.001). When the explainability was high, the purchase intention of the focal product was greater in the accuracy condition (M = 3.84, SD = 0.10) than in the IE condition (M = 3.30, SD = 0.11; F (1,167) = 11.60, p < 0.001).

Mediation by customer inspiration. A 2 (condition) × 2 (explainability) ANOVA revealed a main effect of the degree of explainability (F (1,167) = 187.55, p < 0.001), such that participants reported greater customer inspiration when the recommended item was low explainability (M = 5.04, SD = 0.09) instead of high explainability (M = 3.23, SD = 0.09). There was also an effect of information encountering (F (1,167) = 70.67, p ≤ 0.001), such that participants reported greater customer inspiration when the recommended item was randomly (M = 4.69, SD = 0.09) instead of deterministically (M = 3.58, SD = 0.08) encountered. The interaction was significant as well (F (1,167) = 48.16, p ≤ 0.001). When the item was recommended without explainability to a certain extent, participants reported greater inspiration in the IE condition (M = 6.14, SD = 0.14) than in the accuracy condition (M = 3.95, SD = 0.11). When the item was recommended with some hints, the customer inspiration in the IE condition (M = 3.25, SD = 0.13) was marginally higher than in the accuracy condition (M = 3.21, SD = 0.13; see Figure 4).

We conducted a bootstrapping moderated mediation analysis using the condition (IE vs. accuracy) as the independent variable, the degree of explainability as the moderator, and customer inspiration as the mediator for the purchase intention we measured (PROCESS Model 8). For focal product purchase intention, the index of moderated mediation was significant (index = −0.06; 95% CI: [−0.11, −0.03]. When the explainability was low, the pathway to purchase intention was significant, and the pathway to purchase intention through customer inspiration was significant (β = 0.28, SE = 0.54, 95% CI: [0.18, 0.39]). When there was a high level of explainability, the pathway to purchase intention through customer inspiration was significant as well (β = 0.78, SE = 0.30, 95% CI: [0.21, 0.14]; see

Table 4. Total effect, direct effect, and indirect effect.

	Effect	SE	t	p	LLCT	ULCT
Total effect	0.321	0.052	6.270	<0.001	0.220	0.422
Direct effect	0.129	0.045	2.829	<0.01	0.038	0.218
Indirect effect	0.192	0.038	/	/	0.117	0.268

and

Table 5. Conditional indirect effects of EPI on focal product purchase intention.

Explainability	Effect	SE	LLCT	ULCT
−1.4477	0.280	0.541	0.186	0.396
0	0.179	0.035	0.116	0.254
1.4477	0.785	0.309	0.217	0.143
Moderated mediation	−0.069	0.018	−0.110	−0.039

).

3.3.3. Discussion

Study 3 further confirmed the moderating role of explainability in the impact of encountering product information on focal purchase intentions. Compared to the strategy of high explainability, adopting low explainability had a more significant effect on improving the impact of encountering product information on customer inspiration, which enhanced the subsequent impact on the intention to purchase the focal product. These findings suggest that putting too much emphasis on algorithmic explainability in recommender systems may actually hinder customers’ speculation and association of inconsistent recommended items (encountering products), inhibiting the generation of customer inspiration and subsequent purchase behavior. Study 3 supported the conclusions drawn in Studies 1 and 2, further strengthening their external validity.

4. Discussion

4.1. Conclusions

The study conducted an in-depth analysis of encountering product information in recommendation systems and its impact on purchase behavior. We proposed that EPI positively affects the intention to planned or partially planned purchase (focal product), and that customer inspiration mediates the effect. Further, we found that the moderator of explainability would attenuate this effect. Specifically:

• Encountering product information has a favorable effect on the desire to purchase the focal product, with customer inspiration serving as the underlying psychological mechanism. In contrast to the absence of EPI (as seen in an accuracy-oriented recommender system), EPI can effectively encourage customers with premeditated consumption habits to identify and acquire the focal product. This discovery is intrinsically linked to customer inspiration. Drawing from the Theory of Knowledge Accessibility, EPI that surpasses expectations and stands out has the potential to captivate and motivate customers with relevant background knowledge to recognize its worth, evoke association, and ultimately spark the drive to make the purchase;
• The level of explainability in a recommender system plays a crucial role in how product information affects customer inspiration and purchase intent. Our research shows that utilizing a low-explainability strategy results in a more significant positive impact on both, compared to a high-explainability approach.

4.2. Theoretical Implications

• This study has contributed to marketing research by enhancing our understanding of the relationship between encountering product information and customer decisions. Previous research in the marketing field has highlighted the importance of unexpectedness in IE, which is considered a positive, unexpected occurrence that brings about feelings of surprise or luck [14]. However, our study highlights the significance of encountering information as a valuable resource in itself, revealing hidden connections or analogies between unrelated things. This can aid customers with prepared minds to make informed product decisions, streamline their searches, and reduce decision-making time [21,22];
• The previous literature on the impact of IE on customer decision-making behavior has concentrated solely on impulsive purchases. These studies suggested that IE can encourage customers to prefer the products encountered and ultimately increase their intention to purchase them [6]. Nevertheless, this study revealed that unexpected encounters can positively impact intentional purchases. In particular, this research demonstrated that information obtained from unplanned encounters can effectively motivate customers to promptly and confidently choose the desired product from an overwhelming array of recommended options;
• This study seamlessly merges two theories, knowledge accessibility and customer inspiration, to explore the impact of encountering product information on customer decision-making. Prior research on customer inspiration identified arousal, transcendence, and motivation as key components. Arousal typically stems from external stimuli that intersect with customers’ existing knowledge, experience, and common sense. However, less attention has been paid to customers’ own background knowledge. This study introduced knowledge accessibility theory to enrich our understanding of how background knowledge and external stimuli interact to produce customer inspiration and impact subsequent behavior [35].

4.3. Pratical Implications

The findings of this study have some practical value, particularly in providing guidance for platform managers on how to effectively optimize and design the platform recommendation system, as follows:

• By actively adopting a serendipity-oriented recommender system, e-commerce platforms can make full use of encountering product information to reduce the amount of search friction on the platform [42] in order to achieve higher efficiency and improve profit margins. To optimize recommender systems, platform managers should consider increasing the flexibility of recommendations. When customers are overwhelmed by a large number of items, the system should proactively suggest products that may not have a high match with the current search terms but align with the user’s profile [43]. This approach not only enhances the unpredictability of recommendations but also piques customers’ curiosity, increases the appeal of recommended items, and raises their perceived value. By introducing some inconsistency into the product selections, customers may be inspired to explore further, ultimately leading to increased consumption of focal products;
• Explainability, a mitigator of the algorithmic black-box effect, should be carefully reduced in serendipity-oriented recommender systems. In this study, customer inspiration was found to be an underlying mechanism for the intention to purchase the focal product by encountering product information; however, a higher degree of explainability may trigger customers to attribute the role of the platform or back-end managers, which in turn reduces the traceability and associations of the differences between the encountered product and the searched product, and inhibits the positive effect of customer inspiration in the effect of EPI;
• By incorporating encountering information into recommender systems, we can enhance customer protection. Although our research was focused on the perspectives of platforms and retailers rather than customers, encountering information does improve customers’ experiences. This information helps protect customers from “filter bubbles” and expands their horizons by allowing them to explore areas they may have overlooked due to the precision-focused nature of recommender systems;
• Serendipity-oriented recommender systems ensure diversity in product offerings and support the visibility and sales of both long-tail and cold-start products, benefiting the health of sellers.

4.4. Limitations and Future Research

While this study provides a comprehensive analysis of the positive impact of encountering product information on planned purchases, there is still untapped potential for further research into the effects of EPI on customer behaviors. Moving forward, there are numerous valuable research questions to explore in this area.

• In order to fill the gap in the research of planned consumption caused by information encountering, this study focuses on the perceived value of encountering product information itself as a pre-variable for enhancing the intention to purchase focal products. However, encountering product information (content) is inherently embedded within information encountering (event). Future research could consider whether planned customers, when simultaneously influenced by the unexpectedness of information encountered and the value of encountering product information, have more intent to make unplanned consumption decisions (encountering product purchases) or to reinforce planned consumption goals and quickly make planned consumption decisions (focal product purchases);
• While this study has confirmed that customer inspiration plays a mediating role in the effect of EPI on focal product purchase intention, there may be additional mechanisms at play that can more fully explain this effect. For example, how does encountering product information help customers to identify their desired products more efficiently (target identification) and reject competing products more quickly (competitor rejection), ultimately leading to smoother transactions [44]?
• As per current platform practices, the sources of encountering product information are becoming increasingly diverse. Along with the algorithm-generated product information curated by platform administrators through supplementary tools like the “Random Information Node Generator” [45], there is also encountering product information from user profile data and that shared by others with added social elements. This presents an opportunity for future research to examine the impact of encountering product information from diverse sources on customer behaviors.