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Social Network Integration and User Content Generation: Evidence from Natural Experiments
Abstract
This study examines how social network integration (i.e., integration of online platforms with other social media services, for example, with Facebook or Twitter) can affect the characteristics of user-generated content (volume and linguistic features) in the context of online reviews. Building on the social presence theory, we propose a number of hypotheses on how social network integration affects review volume and linguistic features of review text. We consider two natural experiments at leading online review platforms (Yelp.com and TripAdvisor.com), wherein each implemented a social network integration with Facebook. Constructing a unique panel dataset of online reviews for a matched set of restaurants across the two review sites, we estimate a difference-in-differences (DID) model to assess the impact of social network integration. We find that integration with Facebook increased the production of user-generated content and positive emotion in review text, while simultaneously decreasing cognitive language, negative emotion, and expressions of disagreement (negations) in review text. Our findings demonstrate that social network integration works as a double-edged sword. On the one hand, integration provides benefits in terms of increased review quantity. On the other hand, these benefits appear to come at the cost of reduced review quality, given past research which has found that positive, emotional reviews are perceived by users to be less helpful. We discuss the implications of these results as they relate to the creation of sustainable online social platforms for user content generation.
... By linking services, integrations enhance accessibility and create new revenue streams. For example, Google's integration of YouTube with Search refines content recommendations, reinforcing creator-audience relationships while increasing ad revenue [14,24]. ...
... Social media platforms have historically emphasized content visibility over direct cross-platform engagement. Facebook's early integrations with TripAdvisor and Yelp enabled content sharing through Facebook but did not support interaction between users across different services [4,14]. Similarly, Facebook and Instagram facilitate content distribution while maintaining distinct user bases, leveraging network effects to increase audience reach [1]. ...
... To construct a control group, we will survey Mastodon users who were unaware of Threads. This will help determine whether increased status update activity results from platform integration, providing insights into how interconnected ecosystems shape user engagement [14]. Furthermore, we will examine whether trust-building behavior influences selfreferencing in status updates, assessing trust as a causal factor in how users present their platform affiliation. ...
Traditional social media platforms, once envisioned as digital town squares, face growing criticism over corporate control, content moderation, and privacy concerns. Events such as Twitter's acquisition (now X) and major policy changes have driven users toward alternative platforms like Mastodon and Threads. However, this diversification has led to user dispersion and fragmented discussions across isolated social media platforms. To address these issues, federation protocols like ActivityPub have been adopted, with Mastodon leading efforts to build decentralized yet interconnected networks. In March 2024, Threads joined this federation by introducing its Fediverse Sharing service, which enables interactions such as posts, replies, and likes between Threads and Mastodon users as if on a unified platform. Building on this development, we introduce FediverseSharing, the first dataset capturing interactions between 20,000+ Threads users and 20,000+ Mastodon users over a ten-month period. This dataset serves as a foundation for studying cross-platform interactions and the impact of federation as two separate platforms integrate. The data and code will be available upon acceptance.
... A relatively recent addition to online review websites is the social network integration (SNI) feature (Huang et al., 2017). SNI allows users to link their review website accounts with popular social networking platforms like Facebook, Google, and Twitter (as illustrated in Figure 1). ...
... Typically, SNI offers users the convenience of registering (and authenticating) themselves using their existing social networking accounts and sharing their reviews posted on review websites with these integrated social networking platforms. Although SNI streamlines account registration, login, and the review-sharing process, it also compromises reviewers' anonymity to some extent, as their identity information on the connected social networking account can be accessed through the online review platform (Huang et al., 2017). Previous research has also revealed that SNI can impact the process of creating online reviews by, for example, influencing reviewers' linguistic styles (Fredheim et al., 2015;Huang et al., 2017). ...
... Although SNI streamlines account registration, login, and the review-sharing process, it also compromises reviewers' anonymity to some extent, as their identity information on the connected social networking account can be accessed through the online review platform (Huang et al., 2017). Previous research has also revealed that SNI can impact the process of creating online reviews by, for example, influencing reviewers' linguistic styles (Fredheim et al., 2015;Huang et al., 2017). ...
... The authors infer from these cues that reviewers compete for attention and reputation by choosing when and how to review. Other work identifies the communication strategies that consumers use to garner social approval and obtain rewards when writing reviews, such as writing in a narrative form or using directive speech acts, humor, vivid details, or emotional language (Huang, Hong, and Burtch 2017;Vásquez 2012;Villarroel et al. 2017). Relatedly, Penttinen, Ciuchita, and Caic (2022) propose that reviewers might disclose personal information, use a conversational style, create the illusion of responsiveness, and refer to previous communications to enhance the perceived credibility of their reviews. ...
... The first pertains to the ways reviewers' individual differences-sociodemographic, psychological, or motivational-shape why and how they review (e.g., Chakraborty et al. 2022;Hong et al. 2016;Munzel and Kunz 2014;Ravula et al. 2023). The second entails reviewers' strategic use of language to express themselves and achieve certain goals (e.g., Huang et al. 2017;Shen et al. 2015). ...
... Rather than conceptualizing reviewing as an activity that is commonly understood by all but performed differently because of individual psychological and motivational differences or strategic goals, Third, our novel conceptualization of reviewing orientation informs how consumers aptly and strategically use language. Prior work in this area explains that consumers use certain language to convey aspects of their experiences (e.g., emotional, hedonic, or spiritual dimensions; Kronrod and Danziger 2013;van Laer and Izberk-Bilgin 2019), to compete for attention, or to obtain recognition and rewards (Huang et al. 2017;Shen et al.2015;Vásquez 2012;Villarroel et al. 2017). Our findings complement this work by addressing two key underlying assumptions. ...
We identify three profiles of consumers writing online reviews: 1) the Communals, who write spontaneous and emotional step-by-step account of their peek experiences; 2) the Systemics, who write objective and factual report of each of their experiences; 3) the Competitors, who write authoritative and instructive guidelines about the market.
... The authors infer from these cues that reviewers compete for attention and reputation by choosing when and how to review. Other work identifies the communication strategies that consumers use to garner social approval and obtain rewards when writing reviews, such as writing in a narrative form or using directive speech acts, humor, vivid details, or emotional language (Huang, Hong, and Burtch 2017;Vásquez 2012;Villarroel et al. 2017). Relatedly, Penttinen, Ciuchita, and Caic (2022) propose that reviewers might disclose personal information, use a conversational style, create the illusion of responsiveness, and refer to previous communications to enhance the perceived credibility of their reviews. ...
... The first pertains to the ways reviewers' individual differences-sociodemographic, psychological, or motivational-shape why and how they review (e.g., Chakraborty et al. 2022;Hong et al. 2016;Munzel and Kunz 2014;Ravula et al. 2023). The second entails reviewers' strategic use of language to express themselves and achieve certain goals (e.g., Huang et al. 2017;Shen et al. 2015). ...
... Rather than conceptualizing reviewing as an activity that is commonly understood by all but performed differently because of individual psychological and motivational differences or strategic goals, Third, our novel conceptualization of reviewing orientation informs how consumers aptly and strategically use language. Prior work in this area explains that consumers use certain language to convey aspects of their experiences (e.g., emotional, hedonic, or spiritual dimensions; Kronrod and Danziger 2013;van Laer and Izberk-Bilgin 2019), to compete for attention, or to obtain recognition and rewards (Huang et al. 2017;Shen et al.2015;Vásquez 2012;Villarroel et al. 2017). Our findings complement this work by addressing two key underlying assumptions. ...
Reviewing products and services is a widespread consumer activity in which millions engage. Why and how do consumers review differently from one another? Prior work assumes that consumers commonly understand what reviewing is. Consequently, it attributes differences in reviewing to individual variations in psychological, motivational, and sociodemographic characteristics, consumption experiences, and expertise. This central assumption is problematic because it fails to consider that differences in how consumers understand reviewing may explain why they approach and perform reviewing differently. To address this gap, we analyze a large qualitative dataset composed of reviews and interviews with their authors. Our insights complement prior work by theorizing the sociocultural shaping of reviewing. We answer why consumers review differently by inductively theorizing the concept of reviewing orientation—a cultural model comprising a set of interconnected characteristics that shapes how consumers review and translates into a distinct reviewer voice—a reviewer’s standpoint expressed within a review. We answer how consumers review differently by developing three reviewing orientations: communal sharing, systemic evaluation, and competitive punditry. Finally, we discuss the transferability of the findings, the role of institutional dynamics in reviewing, and recommendations for online review platforms and marketers.
... The emerging SNE functions have been applied by many platforms and applications. SNE enables a partner platform to access and make use of SNS (e.g., Facebook and WeChat) profile information, including the user's name and social connections (Huang, Hong, & Burtch, 2017). Players' gaming platform identity and social network identity thus become intertwined. ...
... Social media enables users to express their personal information (e.g., details of daily life and preferences) and communication network connections that were once invisible visible to others (Wong et al., 2021). The visibility functions of social media have been previously emphasised in the IS literature in other contexts including enterprise social media in organisations (Leonardi, 2015), social network integration in online reviews (Huang, Hong, & Burtch, 2017), or in cyberbullying behaviour (Wong et al., 2021). ...
... Network visibility is understood as social media that affords users the ability to make their social identity and communication networks viewable and public to others (Chen, Wei, & Rice, 2020). For instance, SNE offers TripAdvisor users to identify their Facebook friends who also use the application in the "Your Facebook Friends" taskbar and know their friends' travel information (Huang, Hong, & Burtch, 2017). In this research, we propose information transparency and identity transparency to represent the two dimensions of visibility afforded by SNE. ...
Social factors play a critical role in motivating player participation and commitment to online multiplayer games. Many popular mobile massively multiplayer online games (MMOGs) adopt social network embeddedness (SNE) functions to optimise players' social play experience. SNE changes the traditional pattern of MMOG social play by porting acquaintance relationships (e.g., Facebook friends) from social networking sites to the virtual game world. However, little understanding exists on how SNE impacts mobile MMOG players' game participation results such as play performance and play frequency. Drawing on the affordance framework and social capital literature, this research proposes a theoretical model that integrates the factors of SNE technology affordance (identity transparency and information transparency), players' social experience (social interaction, social support, shared vision, and social pressure), plus affordance effects (play performance and play frequency). The model was validated through a longitudinal field study, in which both subjective and objective data were collected from Game for Peace players. Our findings indicate that identity transparency and information transparency positively correlate with social interaction, social support, shared vision, and social pressure, which, taken together, significantly affect play frequency. The results also show that social interaction and shared vision positively impact players' play performance. The study enhances the theoretical understanding of social relationships in players' game participation results from the SNE aspect. Finally, we lend insights on how game operators can improve player game experience and stickiness.
... Over the years, online review systems have been important information sources for consumers making purchasing decisions (e.g., [1,2]). However, information overload-which prevents review readers from processing the sheer number of reviews available-has become increasingly common more recently, especially among large online review platforms [3]. ...
... Based on these characteristics, higher weight is placed on principle 2 (minimizing cognitive effort to make a choice) and principle 3 (minimizing negative emotion when making a choice). 1 With respect to principle 2, review readers are likely to opt for either of two options, conditional on casting a vote: vote information-rich reviews as helpful or vote information-poor reviews as unhelpful, but not both. Assuming disjunct sets of helpful and unhelpful reviews, indicating one of the options for a subset of reviews automatically classifies the remaining reviews into the other subset. ...
Evaluating the helpfulness of online reviews supports consumers who must sift through large volumes of online reviews. Online review platforms have increasingly adopted review evaluating systems, which let users evaluate whether reviews are helpful or not; in turn, these evaluations assist review readers and encourage review contributors. Although review helpfulness scores have been studied extensively in the literature, our knowledge regarding their counterpart, review unhelpfulness scores, is lacking. Addressing this gap in the literature is important because researchers and practitioners have assumed that unhelpfulness scores are driven by intrinsic review characteristics and that such scores are associated with low-quality reviews. This study validates this conventional wisdom by examining factors that influence unhelpfulness scores. We find that, unlike review helpfulness scores, unhelpfulness scores are generally not driven by intrinsic review characteristics, as almost none of them are statistically significant predictors of an unhelpfulness score. We also find that users who receive review unhelpfulness votes are more likely to cast unhelpfulness votes for other reviews. Finally, unhelpfulness voters engage much less with the platform than helpfulness voters do. In summary, our findings suggest that review unhelpfulness scores are not driven by intrinsic review characteristics. Therefore, helpfulness and unhelpfulness scores should not be considered as two sides of the same coin.
... To address these two research questions, we collected data from a leading online health Q&A community in China, and then we coded the variables using Linguistic Inquiry and Word Count (LIWC, Pennebaker et al., 2001), a tool that has been used in many fields, including information systems literature (Huang et al., 2017;Zhang et al., 2021). We applied a replier-level fixed-effects negative binomial regression model in the main test and addressed several possible endogeneity problems in the robustness check. ...
... To construct measures of these independent variables, we leveraged the latest version of Linguistic Inquiry and Word Count (the Chinese version), a language-analysis tool for investigating the relationship between word usage and psychological variables (Pennebaker et al., 2001). LIWC has been widely used in psychology literature and recently has been increasingly used in information systems and marketing literature (Yin et al., 2016;Wang et al., 2019), especially to quantify emotional expression and LSM in various types of user-generated content (Huang et al., 2017;Peng et al., 2020;Zhang et al., 2021). ...
Given the unwieldy glut of information in online health question-answer (Q&A) service, it is essential to understand what constitutes helpful answers in the medical domain. Despite the fact that studies have examined the impacts of answer content factors on answer helpfulness, there are two gaps that need further analysis. First, the empirical results of the existing relevant studies on the effect of answer emotion are inconsistent. Second, prior studies only have examined the independent impacts of answer content factors and question content cues on answer helpfulness. To fill this gap, a research model reflecting the impacts of emotional content and question-answer congruence on answer helpfulness was developed and empirically examined. Our empirical analyses confirm that emotional content and answer helpfulness are related to one another in the form of an inverted U-shape and indicate that two types of question-answer congruence (emotional intensity congruence and linguistic style matching) positively affect answer helpfulness. Theoretical and practical implications are discussed.
... Numerous studies have explored the impact of online reviews on individual behavior and market performance [3,4]. As online interaction becomes more essential in the digital age, consumers' demands for eWOM services are no longer restricted to one-way interactions, many platforms have integrated social functions, introducing a two-way interactive function, such as customer Q&As [5]. ...
... To extract the affective linguistic features from eWOM content, we processed the full text of online reviews and Q&As using the LIWC program [58]. LIWC measures the number of words in a given text that reflect particular linguistic or psychological processes and spoken language categories, which has been widely used in IS and marketing research [5,42]. ...
Online reviews and customer Q&As have emerged as two vital forms of electronic word-of-mouth (eWOM) that significantly influence consumer decisions in e-commerce. Yet, a comprehensive understanding of the individual and combined roles of these eWOM types in shaping market dynamics remains elusive. This study addresses this research gap by tracking and analyzing three months of eWOM and sales data for 120 laptops on Amazon, comprising 7,205 online reviews, 6,365 customer Q&A questions, and 7,419 answers. Leveraging the Panel Vector Autoregression (PVAR) model and STATA16.0 software, we unravel the intricate dynamics between online reviews, customer Q&As, and laptop sales. The empirical results reveal distinctive influence mechanisms of online reviews and customer Q&As on product sales, with review volume and answer valence positively affecting sales. Importantly, answer volume was found to stimulate online reviews and enhance their valence. Our study elucidates the interplay among online reviews, customer Q&As, and product sales, underscoring the need for future research on multi-type eWOM. Further, the insights gleaned offer valuable guidance for online platforms and retailers to strategize their eWOM management.
... To rule out spurious correlations, we conducted several falsification tests (see Table 5). Specifically, as there is no theoretical rationale to explain why employer responsiveness should be associated with the occurrence of common words, numbers, or types of punctuation in employer reviews, we would not expect to observe a significant effect on these measures (see Huang et al. 2017). We used the Linguistic Inquiry and Word Count (LIWC) software to estimate the prevalence of grammatical and psychological categories in the text by matching words with predefined content dictionaries (Pennebaker et al. 2015). ...
Employer reviews (i.e., online workplace ratings authored by employees) attract broad interest and shape the opinions of potential employees. Thus, companies face the challenge of dealing with these workplace judgments that are outside their direct control. While prevailing theoretical perspectives suggest that responding to third‐party judgments may be an effective way for companies to deal with them, they focus on responses to negative judgments that threaten companies' reputations. Based on signaling theory, we argue that employer responsiveness, signaled by an employer's first response to an employer review, serves as a mechanism of indirect control over employer reviews. Applying a difference‐in‐differences approach—a statistical technique to estimate the causal effect of the treatment (i.e., responsiveness) by comparing it to a control group without the treatment—on a sample of 298,269 reviews of 21,099 employers over 45 quarters posted on the employer review website Kununu, we hypothesize and find that compared to nonresponsive employers, responsive employers receive more diverse (i.e., variety of topics covered) and extensive (i.e., amount of information provided) employer reviews. These effects are especially pronounced in the case of negative employer reviews. Our findings can serve as a guide for employers in dealing with third‐party judgments by demonstrating that employer responsiveness is a valuable signal enhancing online review quality. We contribute to the growing body of HRM research on employer reputation on social media, a critical factor influencing recruitment and retention outcomes. Our study opens new avenues for research to explore the role of responsiveness as a strategic signal in employer brand management.
... Third, since the outcome variable in our study is a count variable, we revise our main model to a fixed-effect Poisson model [26]. As shown in Table 4, the results are consistent with our main model results, which further validate our main findings. ...
The persistent preference of patients for well-known healthcare providers inevitably leads to demand imbalances within the healthcare sector. Luckily, our research reveals that online healthcare platforms (OHPs) can address this issue by offering non-diagnostic digital services. Using an OHP-based online prescribing service as an example, our study reveals that this non-diagnostic digital service has resulted in a significant increase in consultations, particularly for less prominent hospitals and junior physicians. This shift promotes a more equitable distribution of healthcare demand. Furthermore, we demonstrate that the balancing effect primarily arises from attracting low-uncertainty demand-consultations that focus less on diagnosis-and from fostering trust between patients and physicians. Our findings present a compelling information systems (IS) solution to the pressing issue of patient demand imbalance. By illustrating how a non-diagnostic digital solution can transform healthcare delivery and enhance resource allocation, we contribute to improved healthcare efficiency. This paper offers insights for researchers focused on OHP design and for health practitioners seeking to leverage digital solutions to manage demand.
... Recent studies have highlighted the role of big data in personalization. By leveraging data from social media, booking patterns, and online reviews, tourism businesses can gain insights into customer behavior and preferences (Huang et al., 2017). This data-driven approach enables the creation of highly personalized marketing campaigns and service offerings, enhancing customer satisfaction and loyalty (Li et al., 2018). ...
Purpose – This study aims to conduct a thorough and systematic evaluation of the literature in tourism and hospitality to explore the latest advancements and innovative technologies that are reshaping personalized services within the realm of neurotourism. Methodology/Design/Approach - Data for this research was mined, using www.dimensions.ai, and analyzed using computer software called Citespace. Findings – The research established a paucity of neurotourism research in tourism and demonstrated that several challenges including cost and expertise make it difficult to address arising literature on mindfulness tourism. Originality of the research – the integration of neuromarketing, brain research, and neuroscience has the potential to revolutionize tourism marketing by introducing personalized approaches to services and products, overcoming the limitations of traditional market research in understanding human decision-making within the tourism industry. The integration of neuroscientific methods provides a paradigm shift in tourism marketing, enabling marketers to create personalized and emotionally resonant experiences. This shift extends beyond traditional methods, offering a deeper understanding of tourist behavior and desires. In conclusion, the ability to personalize experiences, leverage VR technologies, and adapt in real-time to changing preferences positions brain research is a key driver for the future of tourism marketing.
... The proliferation of online platforms has facilitated the emergence and development of digital discussions that have extended beyond mere consumer experiences (Huang et al., 2017). Similar to trends of consumers posting online reviews of their experiences with products and services, employees share their work experiences at their current or previous companies on the Internet (Mathieu and Babiak, 2016;Lee, 2020). ...
Purpose
Online employee reviews have emerged as a crucial information source for business managers to evaluate employee behavior and firm performance. The purpose of this paper is to test the relationship between employee personality traits, derived from online employee reviews and job satisfaction and turnover behavior at the individual level.
Design/methodology/approach
The authors apply text-mining techniques to extract personality traits from online employee reviews on Indeed.com based on the Big Five theory. They also apply a machine learning classification algorithm to demonstrate that incorporating personality traits can significantly enhance employee turnover prediction accuracy.
Findings
Personality traits such as agreeableness, conscientiousness and openness are positively associated with job satisfaction, while extraversion and neuroticism are negatively related to job satisfaction. Moreover, the impact of personality traits on overall job satisfaction is stronger for former employees than for current employees. Personality traits are significantly linked to employee turnover behavior, with a one-unit increase in the neuroticism score raising the probability of an employee becoming a former employee by 0.6%.
Practical implications
These findings have implications for firm managers looking to gain insights into employee online review behavior and improve firm performance. Online employee review websites are recommended to include the identified personality traits.
Originality/value
This study identifies employee personality traits from automated analysis of employee-generated data and verifies their relationship with employee satisfaction and employee turnover, providing new insights into the development of human resources in the era of big data.
... This unique condition enables us to use difference-in-differences (DID) estimation to identify the causal effects of investor panels on startups' fundraising performance by simulating a natural experiment. The DID approach is a commonly used quasi-experimental estimation that can effectively identify causal relationships by determining the difference between the treatment group (i.e., campaigns that adopted the investor panel) and control group (i.e., campaigns that did not adopt the investor panel) before and after investor panel adoption [59], [60]. ...
Equity crowdfunding has become an increasingly popular financial source for startups. However, most equity crowdfunding investors are nonprofessionals and thus inexperienced. To help inexperienced investors fund startups, which are high risk, equity crowdfunding platforms have designed various effective information disclosure mechanisms to protect investors and improve market efficiency. One such mechanism is the investor panel, which is a novel form of interactive information disclosure for investors. We exploit a natural experiment on a leading equity crowdfunding platform to understand the effectiveness of its investor panel feature in the fundraising performance of startups and analyze the effect of its lead investor feature. Results indicate that investor panels can harm fundraising performance. The hindering effect of investor panels is more salient in campaigns without a lead investor than in campaigns with a lead investor. To further uncover the underlying mechanisms, we perform text mining analysis on comments from investors. The results of the analysis reveal that potential investors posted few and negative comments after the launch of the investor panel feature. Our study contributes to the information disclosure research stream of the crowdfunding literature by determining the nuanced effect of interactive information disclosure. Moreover, our study provides practical implications to crowdfunding entrepreneurs and platforms for campaign information management.
... As per the findings, it was clear cut that social media has a tendency for people to rely on information after a browse by their own rather than depending on phone calls and reviews in websites. As stated by (Huang, Hong and Burtch (2017), use of social media allows a user to find information readily as each platform states argumentative results shared by other users. ...
In Sri Lanka, the use of social media for the travel and tourism industry is in its growth stage. Businesses use social media to be discovered and become part of travelers’ engagement in decision-making. This study aims at identifying the impact of social media on the travel decision-making process of travelers. Semi-structured interviews were conducted with 25 hospitality and tourism undergraduates in Sri Lankan universities using cluster and purposive sampling techniques. The collected data were then analyzed by using content analysis to achieve the objectives of the study. The findings revealed a strong effect of social media on university undergraduates’ travel decision-making and their willingness to engage with social media as the mode of information source. Further, the most appropriate social media platforms to promote Sri Lankan tourism destinations were also identified through this study. It is recommended that travelers use diversified information sources active engagement, seek authenticity, and exercise caution when verifying information. Put succinctly, the support of the public and private sectors is verily needed for the stability and utilization of social media for promoting Sri Lankan tourism destinations.
... We measured the sentiment score using the 'Emotional tone' in the Linguistic Inquiry and Word Count (LIWC), which can accurately identify emotions in language use (Tausczik & Pennebaker, 2009). LIWC is a popular text analysis tool in social science studies; it has also been applied in numerous works in IS literature related to sentiment analysis (Huang et al., 2017;Kim et al., 2022;Wang et al., 2022;Yin et al., 2014). ...
... The first branch focused on identifying the factors that affect user content generation since the sustainability of online platforms depends heavily on the continuous contributions of platform users. This line of study found that social capital (Kankanhalli et al., 2005;Ma and Agarwal, 2007;Wasko and Faraj, 2005), audience size (Zhang and Zhu, 2011), financial incentives (Cabral and Li, 2015), identity disclosure (Pu et al., 2020), and platform integration with other social media services (Huang et al., 2016) all play a significant role in determining user content generation. The second branch explored the characteristics of UGC, such as volume, valence, and linguistic features (Godes and Silva, 2012;Goes et al., 2014;Zeng and Wei, 2013). ...
This research aims to analysis factors influencing sustainable e-participation in development projects. The case of a semi-high-speed rail infrastructure project in a south Indian state is examined in this paper. The analysis considers e-participation-related social media content and news items from 2017 to 2022. Results reveal development themes and campaign strategies influence long-term e-participation. The findings demonstrate that government social media initiatives significantly influence knowledge generation in development projects and thus better-informed e-participation. The growing information-seeking culture among e-participants, on the other hand, compels for knowledge resources and the readiness of the initiators to divulge suitable decisions to meet citizens’ requirements. Current research is limited by its concentration on a rail project. The moderators are expected to construct campaign event plans and involve rail initiatives to enable sustainable e-participation. Unlike previous studies that focused on the delays due to e-participation, the current research portrays the improvement of public service delivery.
Online platforms increasingly utilize technologies like artificial intelligence (AI)-empowered tools to reduce consumers’ search costs and simplify decision making. However, these tools often target specific types of information, leading to what we term "search cost reduction for partial information." Although designed to assist consumers, our study highlights their unintended consequence: these tools can induce "cognitive miser" behavior, where consumers focus on easily accessible information while neglecting other critical details. This behavior can ultimately result in poorer decision making. Using a natural experiment on Yelp, we evaluated the impact of its AI-powered image categorization feature, introduced in 2015 to reduce the search costs of review images. Through a difference-in-differences design and text analysis of consumer complaints, we found that this feature negatively affected decision quality. These findings carry important implications for platform managers and policymakers. Although search cost reduction tools can improve efficiency, they also risk biasing consumer attention toward easily accessible information at the expense of holistic decision making. Online platforms could mitigate these effects by complementing AI-empowered search cost reduction features with tools that emphasize information requiring greater cognitive effort, thereby ensuring balanced consumer awareness. We recommend that platform designers carefully evaluate the broader impacts of such tools to better support consumer decision making.
Digital technologies have revolutionized customer service, enabling efficient online complaint resolution. In our research, we investigate the impact of disclosing service provider identities on performance, customer satisfaction, and biases within a customer complaint management platform. In the first large-scale field experiment involving 75,041 customers, 1,280 service providers, and 672 companies, we found that revealing provider identities enhanced performance by reducing anonymity and promoting accountability. This effect was most pronounced among inexperienced providers with greater discretion in handling complaints. Customers benefited from improved service quality and reported higher satisfaction with resolutions. In the second study involving 2,710 customers, we explored the role of ethnic identity in customer satisfaction. Our findings showed that customers reported greater satisfaction with providers from majority ethnic backgrounds, whereas minority customers exhibited lower satisfaction when paired with providers of the same ethnicity. Four additional studies with 1,211 participants delved into the psychological mechanisms driving these effects. Our results highlight the nuanced role of transparency and ethnic cues in shaping customer perceptions. This research offers actionable insights for firms aiming to optimize service interactions by balancing transparency, accountability, and inclusivity in complaint management policies.
An integral component of user participation in online communities is giving “likes” to content posted by others. Meanwhile, online users are often allowed to create a virtual identity unrelated to their real-world identity. The objective of this study is to identify the motivations behind users’ giving “likes” when their virtual identity (i.e., username) is hidden or shown. Specifically, we examine the impact of an exogenous policy change in an online community that made usernames publicly visible. Our results show that users “liked” fewer but higher-quality articles after the policy change, consistent with their protective self-presentation motivation. This study emphasizes the significance of virtual identity, arguing that a virtual identity devoid of real-world information should not be equated with anonymity. It also identifies “liking” as a key channel of self-presentation and underscores the importance of protective self-presentation. For platforms, understanding users’ motivations to give “likes” and the effects of virtual identity disclosure can help refine community policies to encourage quality content engagement. For content creators, our findings suggest they can enhance content engagement by aligning their offerings with the self-presentation goals of their audience.
Blockchain technologies have catalyzed the rise of decentralized autonomous organizations (DAOs), which operate in an incentive network fueled by crypto tokens. In essence, these tokens are imbued with either payment rights (i.e., transactional tokens) or ownership rights (i.e., governance tokens). The decentralized organizational paradigm dismantles the traditional management structure and bring new research opportunities to Operations Management (OM). While the performance of DAOs has been largely examined in current OM literature, the effectiveness of their internal incentive mechanisms—specifically the one that uses ownership as rewards to promote user contributions—remains unclear. Focusing on DAO-enabled virtual communities, we seek to examine whether decentralized ownership provides stronger incentives for user behaviors, such as creation and curation, in comparison to traditional monetary rewards through the lens of psychological ownership theory. We obtained data from Steemit that captures the reward, creation, curation and transaction behaviors of 98,000 users from May 2017 to April 2019. By leveraging the "power-up" action as a shock that increases user ownership shares, we established a quasi-experimental setting. Employing the PSM-DID model, we found that the use of governance tokens is associated with enhanced creation and curation efforts but declined creation novelty, compared to the use of transactional tokens. Our additional analyses further reveal that the incentive effects of governance tokens diminish over time. However, upon the recurrence of the intended choice, these effects become reinforced. Notably, we find that governance token ownership is more strongly associated with curation efforts for users with weaker social ties. Conversely, for users with high reputation scores, their content creation behaviors are less strongly associated with governance token ownership. This study contributes to the burgeoning discourse on blockchain and cryptocurrency from an operational perspective, providing valuable insights for the design of incentive mechanisms in DAOs and advancing our understanding of operational efficiencies and stakeholder engagement in decentralized structures within Operations Management.
This study investigates how tie formation shapes the production of user-generated content on social media, offering insights into enhancing user engagement and alleviating social media fatigue. We conceptualize tie formation as a relationship codevelopment process involving a tie initiator and a respondent. Drawing on social psychology literature, we propose a framework that traces how initiators’ social cognition evolves throughout this process—from proactively extending an inviting social tie to receiving a responsive one in return. Central to our framework is a perceptual mechanism we term relational resonance, which fosters intrinsic motivation and prompts initiators to adjust their content creation. Using data from YouTube, we develop a Bayesian hierarchical model to identify individual creators’ content adjustments, measured by changes in video volume and viewer engagement. Our results reveal that while initiators tend to reduce content production after receiving a responsive tie, the videos they create generate higher engagement, with “like” and “subscribe” rates increasing by 1.1 and 2.9 percentage points, respectively. Remarkably, the receipt of a responsive tie leads to average weekly increases of 66 likes and 183 subscriptions even after accounting for the decrease in video volume, reinforcing the “quality over quantity” principle in content creation strategies. An online experiment further elucidates the mechanism underlying the observed adjustment behavior, providing robust validation for our proposed framework. Implications for research and practice are discussed.
This paper was accepted by D.J. Wu, information systems.
Funding: T. Song was supported by National Natural Science Foundation of China [Grants 71902114 and 72372101].
Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00830 .
Cross-border e-commerce refers to transaction entities belonging to different customs, realizing order placement and electronic payment settlement through e-commerce platforms, and completing transactions through cross-border logistics to circulate goods, a type of international commercial activity.
Purpose
This study aims to explore whether seeking recommendations for doctors from offline word-of-mouth or online reviews influences patient satisfaction after treatment, and how the source of recommendation affects this effect.
Design/methodology/approach
Using a unique dataset of more than three million reviews from a popular Chinese online health community, this study used the coarsened exact matching method and built fixed-effect models to conduct empirical analysis.
Findings
The results suggest that selecting doctors according to recommendations can improve patient satisfaction and mitigate their dissatisfaction when encountering service failures. However, online recommendations were found to be less effective than offline sources in improving patient satisfaction.
Originality/value
This study provides important insights into patient satisfaction and doctor-patient relationships by revealing the antecedents of satisfaction and the potential for improving this relationship. It also contributes to the understanding of how recommendations in the healthcare context can improve patient satisfaction and alleviate the negative impact of service failures.
This study examines the effect of firms’ participation in platform-endorsed review solicitation programs on consumers’ online review generation. We leverage a natural experiment on TripAdvisor, which launched a review solicitation program that allows hotels to collect reviews directly from guests after their stays with the aid of certified connectivity partners. Applying a two-stage difference-in-differences approach to a panel data set of online reviews for a matched set of hotels across TripAdvisor and Expedia, we find that hotels’ participation in the review solicitation program results in a 34.3% increase in review volume, a 0.151 increase in review rating, but a 16.9% decrease in review length. Review solicitation, however, generates a notable negative spillover effect on the volume of organic reviews. Specifically, the volume of organic reviews is reduced by 15.5% after hotels start soliciting reviews. We provide evidence that the motivational crowding-out effect plays an important role in driving this negative spillover. Further analyses reveal that the effects of review solicitation are heterogeneous with respect to hotels of different types and consumers with different demographic and behavioral characteristics. Finally, using a novel structural topic model, we detect a significant shift in review content from specific and concrete topics to general and abstract topics. Our findings suggest that review platforms and firms should be cautious about the unintended negative consequences of review solicitation on consumers’ review generation.
This paper was accepted by Hemant Bhargava, information systems.
Funding: This work was supported by the National Natural Science Foundation of China [Grants 72371192, 72132008, 71872061, and 72061127002] and the Humanities and Social Science Fund of Ministry of Education of China [Grant 22YJA630021].
Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01006 .
Evaluating the helpfulness of online reviews supports consumers who must sift through large volumes of online reviews. Online review platforms have increasingly adopted review evaluating systems, which let users evaluate whether reviews are helpful or not; in turn, these evaluations assist review readers and encourage review contributors. Although review helpfulness scores have been studied extensively in the literature, our knowledge regarding their counterpart, review unhelpfulness scores, is lacking. Addressing this gap in the literature is important because researchers and practitioners have assumed that unhelpfulness scores are driven by intrinsic review characteristics and that such scores are associated with low-quality reviews. This study validates this conventional wisdom by examining factors that influence unhelpfulness scores. We find that, unlike review helpfulness scores, unhelpfulness scores are generally not driven by intrinsic review characteristics, as almost none of them are statistically significant predictors of an unhelpfulness score. We also find that users who receive review unhelpfulness votes are more likely to cast unhelpfulness votes for other reviews. Finally, unhelpfulness voters engage much less with the platform than helpfulness voters do. In summary, our findings suggest that review unhelpfulness scores are not driven by intrinsic review characteristics. Therefore, helpfulness and unhelpfulness scores should not be considered as two sides of the same coin.
The healthcare market struggles with information asymmetry, limiting patients’ ability to make informed hospital choices. Aiming to bridge this gap, review platforms like Yelp have begun displaying hospitals’ clinical quality data alongside consumer reviews. However, our research uncovers that Yelp’s introduction of maternity care clinical quality measures unexpectedly resulted in lower subsequent Yelp ratings for high-quality hospitals with insufficient staffing. Employing precise foot traffic data and transfer deep learning, we discovered that high-quality, yet understaffed, hospitals experienced a surge in patient volume, which strained their resources and diminished patient satisfaction, leading to negative reviews. This finding has significant implications, signaling the unintended consequences of revealing clinical quality measures, including potential financial losses for hospitals because of reduced federal funding. This research not only contributes to our understanding the dynamics of patient satisfaction but also, offers actionable insights for high-quality hospitals to mitigate the negative impacts of unexpected visibility on review platforms. Our research underscores the importance for patients to discern between objective clinical quality measures and self-reported subjective ratings in their decision-making process. This research applies machine learning and transfer deep learning techniques to healthcare analytics, offering a deeper understanding of the interplay between information disclosure, online reviews, patient satisfaction, and hospital management.
Online platforms have emerged as a dominant business model in numerous industries in the new millennium. In light of the substantial and burgeoning body of empirical platform research, this article synthesizes extant studies and identifies the evolution of underlying research methodologies and topics. Building upon a database of 860 empirical online platform papers in premier journals during the first two decades of the new millennium, this article presents a categorization framework based on the online platform type (including search platforms, e‐commerce platforms, online communities, and mobile platforms) and research perspective (including platform participants, platform orchestrators, and platform ecosystems). We provide a critical review of noteworthy trends and highlight directions for future research in each category of the proposed framework. A comprehensive bibliometric analysis is then conducted to visualize and track scholarship in empirical online platform research. Lastly, we adopt an interdisciplinary lens to synthesize our critical review of empirical online platform research into lessons and research opportunities that emerge from multiple disciplines.
Objectives: Our aim was to enhance patients’ access to health resources. We investigated the effect of patients’ inquiry behaviors on doctors’ responses with online health Q&A platforms. Methods: We obtained data from 6588 medical questions and 69,217 answers from a leading online health Q&A platform in China. We conducted ordinary least squares regression with robust standard errors and a series of robustness checks,including the Poisson model and negative binomial regression, as well as alternative measurement methods. Results: We found that longer questions can lead to a decrease in response numbers. However, they also can elicit longer responses from doctors. Additionally, a bounty can incentivize more doctors to engage in and provide longer responses. The results also reveal that a patient’s negative emotions can weaken the impact of question attributes on the response length. Furthermore, for high-severity questions compared to low-severity ones, all the main effects are weakened. Conclusions: When patients post longer questions, this increases response length and decreases answer numbers; providing more bounty attracts longer and more numerousanswers. At the same time, patients with different disease conditions should expect different responses.
We quantify the effects of rewards from peers and from the platform on the quantity, timeliness, and quality of user-generated content (UGC). Users can receive both monetary and non-monetary rewards. Understanding the effectiveness of different types of rewards provides platforms with guidance on how to design reward systems to encourage desired user content generation. Our unique data come from an online board game platform and consist of information on three types of UGC: initiating forum threads, replying to peers' questions, and writing game reviews. Our results show that both monetary and non-monetary peer rewards, i.e., tips and likes, lead to more, longer, politer, more complex, more informative, but less timely content. In contrast, compensation and badges, i.e., the monetary and non-monetary reward from the platform, have the opposite effects. We discuss how platforms can incentivize the production of specific types of UGC and how rewards impact the UGC posting behavior of different types of users, e.g., top contributors or original content creators.
Review-in-review (RIR) is a feature that allows viewers to generate positive or negative evaluations for primary quality evaluations of a product (e.g., ratings and reviews). This study reveals that it can cause social desirability bias in primary ratings: Reviewers who desire social recognition are driven to adjust their ratings (about 7.4% likelihood) to elicit more helpful responses and avoid unhelpful ones. This bias can be shown as distorted conformity to the prior rating distribution or extremity, depending on the RIR types. The model identifies how bias magnitude correlates with users’ social characteristics, thereby identifying vulnerable individuals. Platforms can incentivize less vulnerable users and remind susceptible ones to decrease the bias and can supplement rating conditional on the identified vulnerability extent (e.g., the distribution by the “independent” raters) to mitigate the bias’s impact on rating viewers. The simulation analysis compares the bias under different counterfactual RIR system designs, finding a composite RIR system (e.g., helpful and unhelpful RIRs) partially neutralizes the bias, obviating the need to remove all RIR features. The model further adapts to evaluate underexplored RIRs forms and can provide a “de-biased” metric while preserving individual ratings.
Negation is a sine qua non of every human language but is absent from otherwise complex systems of animal communication. In many ways, it is negation that makes us human, imbuing us with the capacity to deny, to contradict, to misrepresent, to lie, and to convey irony. The apparent simplicity of logical negation as a one-place operator that toggles truth and falsity belies the intricate complexity of the expression of negation in natural language. Not only do we find negative adverbs, verbs, copulas, quantifiers, and affixes, but the interaction of negation with other operators (including multiple iterations of negation itself) can be exceedingly complex to describe, extending (as first detailed by Otto Jespersen) to negative concord, negative incorporation, and the widespread occurrence of negative polarity items whose distribution is subject to principles of syntax, semantics, and pragmatics. The chapters in this book survey the patterning of negative utterances in natural languages, spanning such foundational issues as how negative sentences are realized cross-linguistically and how that realization tends to change over time, how negation is acquired by children, how it is processed by adults, and how its expression changes over time. Specific chapters offer focused empirical studies of negative polarity, pleonastic negation, and negative/quantifier scope interaction, as well as detailed examinations of the form and function of sentential negation in modern Romance languages and Classical Japanese.