Despite hospitality and tourism researchers’ recent attempts on examining different aspects of online word-of-mouth [WOM], its impact on hotel sales remains largely unknown in the existing literature. To fill this void, we conduct a study to empirically investigate the impact of online consumer-generated reviews on hotel room sales. Utilizing data collected from the largest travel website in China, we develop a fixed effect log-linear regression model to assess the influence of online reviews on the number of hotel room bookings. Our results indicate a significant relationship between online consumer reviews and business performance of hotels.

The impact of online user reviews on hotel room sales [Summary]

Social media are fundamentally changing the way we communicate, collaborate, consume, and create. They represent one of the most transformative impacts of information technology on business, both within and outside firm boundaries. This special issue was designed to stimulate innovative investigations of the relationship between social media and business transformation. In this paper we outline a broad research agenda for understanding the relationships among social media, business, and society. We place the papers comprising the special issue within this research framework and identify areas where further research is needed. We hope that the flexible framework we outline will help guide future research and develop a cumulative research tradition in this area.

Introduction to the Special Issue—Social Media and Business Transformation: A Framework for Research

Social media comprises communication websites that facilitate relationship forming between users from diverse backgrounds, resulting in a rich social structure. User generated content encourages inquiry and decision-making. Given the relevance of social media to various stakeholders, it has received significant attention from researchers of various fields, including information systems. There exists no comprehensive review that integrates and synthesises the findings of literature on social media. This study discusses the findings of 132 papers (in selected IS journals) on social media and social networking published between 1997 and 2017. Most papers reviewed here examine the behavioural side of social media, investigate the aspect of reviews and recommendations, and study its integration for organizational purposes. Furthermore, many studies have investigated the viability of online communities/social media as a marketing medium, while others have explored various aspects of social media, including the risks associated with its use, the value that it creates, and the negative stigma attached to it within workplaces. The use of social media for information sharing during critical events as well as for seeking and/or rendering help has also been investigated in prior research. Other contexts include political and public administration, and the comparison between traditional and social media. Overall, our study identifies multiple emergent themes in the existing corpus, thereby furthering our understanding of advances in social media research. The integrated view of the extant literature that our study presents can help avoid duplication by future researchers, whilst offering fruitful lines of enquiry to help shape research for this emerging field.

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Advances in Social Media Research: Past, Present and Future

We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.

Density-based clustering based on hierarchical density estimates

Customer engagement with tourism social media brands

Despite the popular use of social media by consumers and marketers, empirical research investigating their economic values still lags. In this study, we integrate qualitative user-marketer interaction content data from a fan page brand community on Facebook and consumer transactions data to assemble a unique data set at the individual consumer level. We then quantify the impact of community contents from consumers (user-generated content, i.e., UGC) and marketers (marketer-generated content, i.e., MGC) on consumers' apparel purchase expenditures. A content analysis method was used to construct measures to capture the informative and persuasive nature of UGC and MGC while distinguishing between directed and undirected communication modes in the brand community. In our empirical analysis, we exploit differences across consumers' fan page joining decision and across timing differences in fan page joining dates for our model estimation and identification strategies. Importantly, we also control for potential ...

Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content

Despite the popular use of social media by consumers and marketers, empirical research investigating their economic values still lags. In this study, we integrate qualitative user-marketer interaction content data from a fan page brand community on Facebook and consumer transactions data to assemble a unique data set at the individual consumer level. We then quantify the impact of community contents from consumers (user-generated content, i.e., UGC) and marketers (marketer-generated content, i.e., MGC) on consumers’ apparel purchase expenditures. A content analysis method was used to construct measures to capture the informative and persuasive nature of UGC and MGC while distinguishing between directed and undirected communication modes on the brand community. In our empirical analysis, we exploit differences across consumers’ fan page joining decision and across timing differences in fan page joining dates for our model estimation and identification strategies. Importantly, we also control for potential self-selection biases and relevant factors such as pricing, promotion, social network attributes, consumer demographics and unobserved heterogeneity. Our findings show that engagement in social media brand communities lead to a positive increase in purchase expenditures. Additional in-depth examinations of UGC and MGC impacts show robust evidence that social media brand community contents affect consumer purchase behavior through embedded information and persuasion. Evidence also points to the different roles played by UGC and MGC, varying by the type of directed or undirected communication modes by consumers and the marketer. Specifically, the elasticities of UGC information richness are 0.006 (directed communication) and 3.140 (undirected communication), whereas those of MGC information richness are insignificant. Moreover, the elasticity of UGC valence is 0.180 (undirected communication), while that of MGC valence is 0.004 (directed communication). Overall, UGC exhibits a stronger impact than MGC on consumer purchase behavior. Our findings provide various implications for academic research and practice.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2170034_code358840.pdf?abstractid=2048614&type=2

Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. However, DDPMs require hundreds to thousands of iterations to produce final samples. Several prior works have successfully accelerated DDPMs through adjusting the variance schedule (e.g., Improved Denoising Diffusion Probabilistic Models) or the denoising equation (e.g., Denoising Diffusion Implicit Models (DDIMs)). However, these acceleration methods cannot maintain the quality of samples and even introduce new noise at a high speedup rate, which limit their practicability. To accelerate the inference process while keeping the sample quality, we provide a fresh perspective that DDPMs should be treated as solving differential equations on manifolds. Under such a perspective, we propose pseudo numerical methods for diffusion models (PNDMs). Specifically, we figure out how to solve differential equations on manifolds and show that DDIMs are simple cases of pseudo numerical methods. We change several classical numerical methods to corresponding pseudo numerical methods and find that the pseudo linear multi-step method is the best in most situations. According to our experiments, by directly using pre-trained models on Cifar10, CelebA and LSUN, PNDMs can generate higher quality synthetic images with only 50 steps compared with 1000-step DDIMs (20x speedup), significantly outperform DDIMs with 250 steps (by around 0.4 in FID) and have good generalization on different variance schedules. Our implementation is available at https://github.com/luping-liu/PNDM.

Pseudo Numerical Methods for Diffusion Models on Manifolds

The distinct signaling effects of R&D subsidy and non-R&D subsidy on IPO performance of IT entrepreneurial firms in China

Despite the popular use of user and system recommendations by online retailers to drive product sales in e-commerce, empirical research investigating their relative effectiveness and interactions still lags. We attempt to answer: (1) What is the relative impact of user and system recommendations on product sales in e-commerce? (2) Are user and system recommendations substitutes or complements in affecting product sales in e-commerce? Using data on the digital camera category from the largest business-to-consumer platform in China, Tmall.com, we use linear panel data models to examine the impact of user recommendation volume and valence, and system recommendation strength on product sales, controlling for relevant factors at the recommended product, product recommender, product category, and time unit levels. Importantly, we account for implicit sales correlations among products and potential simultaneity between recommendations and sales. We uncover several notable findings. Specifically, a 1% increase in the volume (valence) of user recommendations on a product increases the product's sales quantity by 0.013% (0.022%), whereas a 1% increase in the strength of system recommendations on a product increases the product's sales quantity by 0.006%. Overall, user recommendations are more effective than system recommendations in driving product sales. Furthermore, we find that there is a substitute relationship between user recommendation volume and system recommendation strength. Our findings provide important theoretical contributions and implications for recommendation-based product marketing and e-commerce platform design. 1% increase in user recommendation volume increases product sales by 0.013%.1% increase in user recommendation valence increases product sales by 0.022%.1% increase in system recommendation strength increases product sales by 0.006%.User recommendation is more effective than system recommendation in driving sales.Substitute relationship between user and system recommendations in driving sales.

Zhijie Lin

Papers

Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content

Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content

Pseudo Numerical Methods for Diffusion Models on Manifolds

The distinct signaling effects of R&D subsidy and non-R&D subsidy on IPO performance of IT entrepreneurial firms in China

An empirical investigation of user and system recommendations in e-commerce