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Online product reviews may be subject to self-selection biases that impact consumer purchase behavior, online ratings’ time series, and consumer surplus. This occurs if early buyers hold different preferences than do later consumers about the quality of a given product. Readers of early product reviews may not successfully correct for these prefere...
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... Mục tiêu chính của TAM là cung cấp cơ sở để quan sát tác động của các yếu tố bên ngoài ảnh hưởng đến quyết định chấp nhận công nghệ của người dùng. Cho đến nay, TAM đã được sử dụng rộng rãi trong các nghiên cứu liên quan đến công nghệ thông tin (Li & Hitt, 2008). Nhiều bài nghiên cứu đã ứng dụng mô hình chấp nhận công nghệ (TAM) trong nhiều bối cảnh khác nhau để giải thích sự khác biệt về ý định chấp nhận công nghệ của người tiêu dùng trong từng lĩnh vực. ...
ONLINE PURCHASE INTENTION SUPPORTED BY ARTIFICIAL INTELLIGENCE: THE ROLE OF USEFULNESS AND CUSTOMIZATION
The trend of applying Artificial Intelligence (AI) to e-commerce platforms is becoming popular to attract customers to purchase products and services (Wang et al., 2023). To contribute to this explanation, research was conducted to explain the factors that influence AI-powered online purchase intentions. Through PLS-SEM analysis results from 366 questionnaires, collected from people who have made online purchases in Ho Chi Minh City, show that perceived usefulness and perceived customization affect attitudes towards AI and online purchasing intention. The study also found that the moderating effect of perceived risk did not influence the relationships between perceived usefulness, perceived customization, and online purchase intention. This shows that in the context of AI-powered online purchases, perceived usefulness and perceived customization are still the most important factors influencing consumers' purchase intentions, even when consumers feel risky when purchasing online. Results from the research help add to the theoretical foundation of customer purchasing behavior and suggest management implications for businesses in the field of e-commerce.
... Regarding influencers, the information collected indicates that, although there is an impact of digital influencers on the purchasing decisions of some participants, as mentioned by Schwartz et al. (2016) [83], this type of influence is not widespread among everyone. This divergence of opinion may reflect individual differences in attitudes towards advertising, levels of resistance to consumer stimuli, the ability to distinguish between needs and desires, and the degree of influence that advertising campaigns have on each person, as reported in previous studies (Duan et al., 2008 [194]; Li and Hitt, 2008 [195]), which can positively or negatively influence purchase intention, mostly by an increase in notoriety or persuasion. The persuasion exerted by social networks and digital platforms on consumption is perceived by a significant portion of respondents. ...
This study addresses the challenges faced by the fashion industry and its consumers in relation to sustainability and the circular economy in a digital age, relating them to the impacts that this industry triggers, both environmentally and socially. The aim is to understand the relations between environmental concern, fashion consumer awareness and adaptation to the digital evolution, and how these can result in an intention to buy sustainable fashion. The methodology adopted was based on other studies in the area, allowing us to answer the investigation question and, finally, to present some reflections that could mitigate the problems found. This research concisely highlights the limitations of the current linear economy, demonstrating the difficulties in the transition to a more sustainable world. This article is relevant for researchers in the field of fashion brand communication and consumer behaviour, as the results show some theoretical and experimental content for a better development of strategies and practices in the field of sustainable fashion, taking into account the digital evolution.
... Firstly, the design and user experience of travel websites have a significant impact on consumer perceptions and behaviors. According to research, visually appealing and user-friendly website designs increase user engagement and trust, ultimately leading to higher purchase intentions (Li and Hitt, 2008). Furthermore, the availability of interactive tools like search filters, maps, and user reviews improves the user experience and allows for more informed decision-making (Xiang & Gretzel, 2010Second, the quality and accuracy of information available on travel websites have a huge impact on consumers' confidence and trust in the platform. ...
... A travel website's layout, design, and user interface all play a significant role in determining how users interact with the site and make judgments about what to buy. According to Li and Hitt (2008), the way a website is designed has a significant impact on how users perceive it and behave. In the context of travel websites, design factors such as layout, visual aesthetics, navigation, and user interface influence users' entire browsing experience as well as the platform's perceived legitimacy. ...
... Numerous research studies have demonstrated how website design affects customer behavior and purchase intentions. According to Li and Hitt (2008), aesthetically pleasing website designs increase user trust and engagement, which in turn influences users' intentions to make purchases. A welldesigned website also makes a good initial impression, which has a big impact on customers' propensity to research travel possibilities further, according to a 2003 study by Chen and Dubinsky. ...
This study explores the impact of design, information, and usefulness of travel websites on travel purchase intention. Utilizing a quantitative research design, data from 177 respondents were collected via a structured questionnaire administered through Google Forms. Regression analysis revealed that design, information, and usefulness significantly and positively influenced travel purchase intention with each predictor variable exhibiting a substantial effect on travel purchase decisions. These findings corroborate existing research emphasizing the importance of website design, information quality, and usefulness in shaping consumer purchase intention in the online travel domain. For instance, Kim & Lennon, (2008) found that visually appealing design positively influences purchase intention, while Ruiz Mafe Sanz Blas, (2006) demonstrated the significant impact of informative content on purchase intention, aligning with the positive coefficients observed in this study. KEYWORDS- Travel website design, Information and usefulness of travel website and travel purchase intention
... However, our observation of more extreme ratings on TripAdvisor challenges this assumption, leading us to consider the presence of fabricated ratings. [9] delve into the issue of selection bias in reviewing. Their study reveals a bimodal distribution of ratings for many products, with a propensity towards extreme values. ...
This study delves into the phenomenon of fraudulent online ratings, particularly those fabricated by restaurants targeting their competitors. Given the widespread use of online ratings by consumers in deciding where to dine—restaurants are tempted to deploy deceptive tactics such as posting fake negative reviews on rival establishments. This unethical behavior not only undermines the trustworthiness of genuine ratings but also detrimentally affects consumer welfare. Therefore, our study aims to understand the drivers behind such deceptive practices and their implications on consumer choice. To investigate the prevalence of cheating, we leverage a notable disparity between two prominent online rating platforms—TripAdvisor and OpenTable. By analyzing data from these platforms, we assess various dimensions of competition faced by restaurants, including proximity, price range, and culinary offerings. Our findings reveal a significant correlation between the intensity of competition and instances of cheating among restaurants. Specifically, establishments tend to engage in deceptive practices more frequently when faced with a higher number of competitors operating in the same price range or holding similar rankings. This underscores the influence of economic incentives in shaping the decision-making process regarding cheating behaviors. The study also sheds light on the complex interplay between competition dynamics and fraudulent activities in the realm of online restaurant ratings. Understanding these dynamics is crucial for devising effective strategies to curb deceptive practices and uphold the integrity of online rating systems.
... People often consult online review websites and read reviews by previous consumers when choosing among these options. It is now well-established that online reviews influence buying decisions and thus drive sales and profits (e.g., Archak et al., 2011;Chevalier & Mayzlin, 2006;Li & Hitt, 2008;Netzer et al., 2012;Pavlou & Dimoka, 2006) (for literature reviews, see Sharkey et al. (2023); Tadelis (2016)). Online reviews typically consist of two components: star ratings and written feedback. ...
Online reviews serve as a guide for consumer choice. With advancements in large language models (LLMs) and generative AI, the fast and inexpensive creation of human-like text may threaten the feedback function of online reviews if neither readers nor platforms can differentiate between human-written and AI-generated content. In two experiments, we found that humans cannot recognize AI-written reviews. Even with monetary incentives for accuracy, both Type I and Type II errors were common: human reviews were often mistaken for AI-generated reviews, and even more frequently, AI-generated reviews were mistaken for human reviews. This held true across various ratings, emotional tones, review lengths, and participants’ genders, education levels, and AI expertise. Younger participants were somewhat better at distinguishing between human and AI reviews. An additional study revealed that current AI detectors were also fooled by AI-generated reviews. We discuss the implications of our findings on trust erosion, manipulation, regulation, consumer behavior, AI detection, market structure, innovation, and review platforms.
... When making online purchases, buyers often rely on online reviews to gather more information (Yin et al., 2021). Previous research has shown that online reviews have significant impacts on buyers' expectation and behaviors (Ding et al., 2022;Hu et al., 2017;Li & Hitt, 2008). However, a major concern with online reviews is the presence of self-selection bias (Askalidis et al., 2017;Jha & Shah, 2021;Piramuthu et al., 2012;Wu et al., 2020;Zhou & Guo, 2017). ...
... The existing studies have identified two main types of self-selection bias, acquisition bias and underreporting bias. It has been found that acquisition bias, where buyers with a stronger preference for a product are more likely to make a purchase, can result in positively biased reviews (Li & Hitt, 2008). In comparison, underreporting bias occurs when buyers are more motivated to write a review with extreme satisfaction or dissatisfaction, resulting in relatively fewer moderate reviews (i.e., a U-shaped distribution of online reviews) (Hu et al., 2006). ...
... To further clarify the positioning of this study, we have compared our research approach with existing selfselection bias studies in Table 1. Li and Hitt (2008) considered different buyer preferences on product quality, but they did not clearly differentiate types of products. ...
Please cite this article as: Y. Xie, W. Yeoh and J. Wang, How self-selection Bias in online reviews affects buyer satisfaction: A product type perspective, Decision Support Systems (2024), https://doi.org/10.1016/j.dss.2024.114199 ABSTRACT Online reviews play a crucial role in shaping buyers' purchase decisions. However, previous research has highlighted the existence of self-selection biases among buyers who contribute to reviews, which in turn leads to biased distributions of review ratings. This research aims to explore the further influences of self-selection bias on buyer satisfaction through agent-based modeling, considering two product differentiations: search and experience differentiation, as well as vertical and horizontal differentiation. Our findings reveal that self-selection bias can have varying positive and negative effects on the usefulness of online reviews in suggesting product quality (i.e., review utility) to buyers, thus affecting buyer satisfaction. While self-selection bias tends to decrease review utility in most scenarios, interestingly, it can also increase review utility by enabling a "screening" function of online reviews in addition to its normal "measuring" function. We also find that the varying effects of self-selection bias on buyer satisfaction are contingent upon the type of products under scrutiny and the interaction of different types of self-selection bias. This research makes valuable contributions to the existing literature on online reviews by introducing a novel theory to explain the effects of self-selection bias on buyer satisfaction. J o u r n a l P r e-p r o o f Journal Pre-proof 2
... For example, the existing literature highlights that the impact of eWOM on the box office performance (i.e., sales) of movies in the first week of launching is different to that in later weeks (e.g., Fan et al., 2021;Hennig-Thurau et al., 2012;Marchand et al., 2017). Furthermore, how consumers react to eWOM and how they provide or create it can also change over the course of a product's life cycle (Li & Hitt, 2008). ...
Electronic word of mouth (eWOM) has evolved dramatically in the past 20 years, and is substantially shaping modern consumer behaviors and altering marketing management dynamics across both consumer and industry markets. We call this evolution "eWOM 2.0", as captured in this Special Issue. Ten research articles advance our understanding in how eWOM drives the continued development of digital communication across B2B and B2C sectors. This Special Issue further contributes to understanding the constantly evolving landscape of eWOM research and practice, and points to the future directions for eWOM investigation and usage. In this editorial, we first outline the reasoning behind this special issue, followed by the summary of the articles, and the reflections on eWOM 2.0. We conclude by outlining future research opportunities that will propel the field further forward.
... Recent research has established that a consumer's behavioral decisions regarding a product may be influenced by other community members' total ratings of that product (e.g., Lee et al., 2015a;Li & Hitt, 2008;Waytz & Epley, 2012;Xie & Lee, 2015). This stream of research has focused mainly on the informational aspects of social influence, according to which people follow others' opinions because they believe the information others possess is valid. ...
2024). "How do consumers make behavioral decisions on social commerce platforms? The interaction effect between behavior visibility and social needs" Information Systems Journal (ISJ) (Abstract The online phenomenon of social commerce (i.e., s-commerce) platforms has emerged as a combination of online social networking and e-commerce. On s-commerce platforms, consumers can observe others' behavioral decisions and can distinguish those made by their friends from those made by their followees (i.e., the people a focal consumer follows but who do not follow that consumer back). Given this distinction, our study examines how consumers' behavioral decisions-regarding, for example, purchases, ratings, or "likes"-are made on s-commerce platforms, with a focus on how they are influenced by prior decisions of friends and followees. Combining panel data from a large s-commerce platform and two controlled experiments, we identify a strong normative social influence pattern in which consumers tend to follow others' prior decisions to gain social approval. Because the occurrence of normative social influence depends on both consumer behaviors with high public visibility and strong consumer needs to establish social ties, the unique information concerning behavior visibility and consumers' social needs in the panel data allows us to identify normative social influence and to distinguish it from informational confounding mechanisms. Our panel data results show that on a friend network, where consumers' behavioral decisions are visible, females exhibit a greater tendency to follow others' prior decisions than males. We attribute this result to the stronger social needs of females. However, on a followee network, where behavioral decisions are invisible, these differences become less evident. Moreover, the two experiments demonstrate that making decision contexts private or activating social needs via a priming procedure can thwart (or even turn off) normative social influence. Our findings challenge prior research that identifies informational social influence as the predominant driver of conformity behaviors and thus have important implications for practice related to normative social influence, such as the development of techniques for satisfying consumers' different social needs depending on their gender or any other situational factors on s-commerce platforms.
... [19] contributed to understanding the self-selection and information role of online product reviews. The rise of mobile social commerce was explored by [20], focusing on the features influencing online impulse buying. [21] conducted a meta-analysis on online review characteristics affecting consumer decision-making, providing valuable insights into the factors that shape customer perceptions. ...
This research delves into the intricate dynamics of employee-customer familiarity and its profound influence on customer purchase intentions within the burgeoning domain of web-based data analytics. In an era characterized by an increasingly digital marketplace, understanding the nuanced interactions between employees and customers is paramount for businesses striving to enhance customer relationships and drive purchase decisions. Drawing on empirical investigations, this study unravels the multifaceted facets of employee-customer familiarity, seeking to shed light on its implications for customer purchase intentions in the context of web-based data analytics. In this paper, an empirical study investigates the influence of employee-customer familiarity on customers' purchase intention for the home bedding industry, summarizes the current situation, puts forward research hypotheses and constructs a model of the effect of employee-customer familiarity on purchase intention was constructed. The familiarity of buyers and sellers was evaluated through a customer questionnaire, which provided subjective insights into the strength of interpersonal relationships. Meanwhile, confidence analysis, ANOVA (analysis of variance), correlation analysis and regression analysis were conducted on the survey data to explore the actual effects of these relationships on customers' purchase intention, and the positive effects of the five hypotheses on purchase intention were investigated. The anticipated findings suggest that increasing employee-customer familiarity positively impacts customers' purchase intentions, thereby illuminating the critical role of personalized interactions in driving business outcomes purchase intention, and the positive effects of the five hypotheses on purchase intention were investigated. The anticipated findings suggest that increasing employee-customer familiarity positively impacts customers’ purchase intentions, thereby illuminating the critical role of personalized interactions in driving business outcomes. Furthermore, the study sought to reveal the nuances of this relationship, recognizing the potential impact of different customer characteristics and industry contexts. Practical implications center on guiding companies in aligning their strategies to improve customer satisfaction and loyalty. From staff training programmes to targeted marketing campaigns, from brand influence to web e-commerce platform optimisation, businesses can use the insights gained from this research to build more meaningful connections with their customers. Building more meaningful connections with customers.
... There has been a significant interest in the biases that can affect the reliability of ratings given by online consumers (e.g., G. Gao et al., 2015;Han & Anderson, 2020;Hu et al., 2017;Jiang & Guo, 2015;X. Li & Hitt, 2008;F. X. Wang & Anderson, 2023). Among these biases, the selfselection bias is particularly prominent, as participating in ORS is a fundamentally voluntary activity. Selfselection bias occurs when a specific category of consumers is more inclined to write reviews. This phenomenon may lead to a distortion in the representation of average opi ...
It has been established in the literature that the number of ratings and the scores restaurants obtain on online rating systems (ORS) significantly impact their revenue. However, when a restaurant has a limited number of ratings, it may be challenging to predict its future performance. It may well be that ratings reveal more about the user who gave the rating than about the quality of the restaurant. This motivates us to segment users into “inflating raters,” who tend to give unusually high ratings, and “deflating raters,” who tend to give unusually low ratings, and compare the rankings generated by these two populations. Using a public dataset provided by Yelp, we find that deflating raters are better at predicting restaurants that will achieve a top rating (4.5 and above) in the future. As such, these deflating raters may have an important role in restaurant discovery.
