Power of information transparency: How online reviews change the effect of agglomeration density on firm revenue
Introduction
Firm agglomeration (i.e., spatial concentrations of organizations) is a common phenomenon across industries [1]. Firms often aggregate or even stay near their competitors to increase their opportunities to reach consumers and reduce the cost of obtaining resource, such as labor and suppliers [2]. This situation is particularly salient in the hotel industry [3]. For example, Home Inn and Hanting Hotel (two China chain brands of Home Inns Hotel Group and the Huazhu Hotel Group) prefer to imitate their rivals' decision to enter a new market or locate in the nearby area [4]. Although firms suffer from competitive pressure in agglomeration because they provide similar products and services, the general belief is that they can gain considerable benefits over the competition costs to achieve competitive advantage [[5], [6], [7]].
To validate such an effect, previous studies have investigated the effect of firm agglomeration on its performance, including survival [8,9], price [10], profit [11], innovation [5], and revenue [7,12]. Majority of these studies have identified the positive effect of firm agglomeration. Agglomeration theory proposes several mechanisms, such as information asymmetry, to analyze the effects of agglomeration [13]. Facing information asymmetry, agglomeration helps consumers reduce information search costs, thereby lowering transaction uncertainty and risks prior to making consumption decisions [13]. As such, a firm prefers to remain close to other firms with high levels of advertising to share consumer demand [7]. Then, the increasing demand and consumption lead to enhanced firm performance [7].
However, the effect of agglomeration may change given that a large amount of information has become easily available in online platforms, thereby minimizing the information asymmetry between firms and consumers while increasing the competition cost of firms. Firms increasingly rely on online reviews of platforms to inform consumers about their existence and the quality of product or service they provide [14]. Compared to the huge expense of advertising, online reviews of platforms are nearly costless but are able to break through geographical and time constraints, thus influencing consumer search patterns and search cost. Large amounts of online reviews enhance consumer awareness of “where is here” and minimize information asymmetry between consumers and firms [15]. Therefore, online reviews may mitigate the agglomeration effectiveness, because the former may substitute the function of agglomeration in terms of reducing searching costs by enabling transparent information. Moreover, online reviews of a focal firm are available not only for consumers but also for other firms located in the nearby area. Such transparent information increases the focal firm's competition cost and changes its agglomeration effectiveness, because competitors nearby can rapidly imitate and learn [16,17]. Previous studies have revealed that online reviews are important indicators of firm performance, because they reflect the reputation and popularity of firms [18]. Nevertheless, how online reviews modify the effect of agglomeration on firm performance remains unexplored. This issue must be addressed, because managers can combine their preferred location decisions or offline agglomeration strategy with online strategies to better promote firm performance.
In the current work, we examine firm revenue, because it is not only a key indicator of performance but also a primary concern of firm managers. In fact, this topic has received great attention in prior literature [7,14,[19], [20], [21], [22], [23]]. This paper focuses on online review quantity and quality, which are critical dimensions of information [24]. Specifically, we investigate two important attributes of online review quantity, namely, review volume and review length. More and longer reviews convey more information, and these attributes have been frequently examined in the literature [21,25]. We also examine online review quality and construct a composite quality-quantity metric based on different aspects (including spelling error and readability) of review texts. The hotel sector is selected as the study setting because agglomeration is very popular and is a common context for hotel managers [3,7]. The hotel industry is also an ideal context because online hotel reviews are important for consumers to make purchase decisions on such high-priced and intangible experience products [26].
In summary, our core research question is as follows: How do online reviews (including quantity and quality) of hotels change the effect of agglomeration density on their revenue?
To answer this question, we draw upon the theoretical lens of information transparency and agglomeration theory to develop our model. A large number of online reviews, especially high-quality ones, represent a high level of information transparency. On the one hand, this minimizes information asymmetry between consumers and firms; on the other hand, this increases competition cost by increasing marketing learning. We collect tax data of hotels from three different sources: the Texas Comptroller of Public Accounts, online review data from the TripAdvisor website, and location data from Google Maps. From these, we construct a unique panel data and use the generalized method of moments approach to estimate the model. We confirm that agglomeration density positively affects firm revenue in the hotel sector. However, such an effect decreases with the increase in consumer online review quantity in terms of review volume and length. Moreover, the moderating effect of high-quality review volume is stronger than that of low-quality review volume. The positive effect of agglomeration density decreases due to the quantity of high-quality (few spelling errors and easy to understand) reviews, but not with low-quality reviews. Furthermore, compared with branded hotels, the negative moderating effects of online review quantity on agglomeration density are stronger for independent hotels but are similar among hotels with different levels of quality.
While previous studies investigate online reviews and offline location separately, the present study is an early attempt to combine agglomeration with online reviews and investigate the changing effects of online reviews on the relationship between agglomeration and firm revenue. We contribute to the existing literature by providing a new perspective of online information. Specifically, we contribute to agglomeration theory by exploring the boundaries of agglomeration economies from an information transparency perspective. Agglomeration theory proposes several explanations to understand the source of agglomeration economies [1]. However, our work reveals the process from agglomeration economies to diseconomies under information transparency environment by clearly demonstrating the mitigating effect of online review quantity on changing the agglomeration effect. We also add novel knowledge to the online review literature by highlighting the moderating roles of two important indicators of online review quantity (i.e., volume and length). Unlike previous studies on the direct impact of online reviews [18,19,[26], [27], [28], [29], [30]], we discover that online review quantity has a substitute effect on the traditional strategy (i.e., agglomeration or geographical clustering) in influencing firm revenue. We further find that such a substitute is effective for the quantity of high-quality online reviews, but not for low-quality. These findings contribute to information transparency theory by simultaneously examining information quantity and quality and by revealing the dual roles (direct and moderating effects) of online reviews from the perspective of information transparency. Finally, our findings present new insights by investigating the roles of hotel chain affiliation and quality in differentiating the effects of agglomeration density and online reviews on revenue. We clearly indicate that independent hotels gain more benefits from agglomeration and online reviews (and its substitution effect) compared to branded hotels.
Section snippets
Advantages and disadvantages of agglomeration
Agglomeration indicates spatial concentrations of diverse or related organizations. Following McCann and Folta [1], the current study focuses on the spatial concentrations of related firms, which produce goods or services that are close substitutes within a particular industry. Previous studies have long recognized firm agglomeration and also proposed agglomeration theory with several mechanisms to understand its occurrence [1,3]. Agglomeration theory suggests that “net benefits of being in a
Hypothesis development
This research focuses on online review quantity and quality but not review valence as the moderating variable. Review quantity and quality are two major measures of information transparency for which Williams [42] propose three criteria: information outcome (quantity), information process (quality), and information infrastructure. The first two indicators are most important, and the increasing quantity of information, especially that of high-quality, reflects higher information transparency.
Data
We collected data from three main sources, namely, Texas Comptroller of Public Accounts, TripAdvisor (a major U.S. online travel platform), and Google Maps. The Texas lodging sector plays an important role in the state's economy given that Texas is the second largest state. The Texas Comptroller of Public Accounts provides a complete record of quarterly tax data for the lodging sector in the area. It also includes basic information, such as hotel name, address, and capacity. Tax involves
Main results
Table 4 presents the empirical results. The baseline model without the influence of online reviews is shown in Column (1) of Table 4, whereas the full models, including the main and moderating effects of online review volume, is shown in Columns (2) in Table 4. Each model shows consistent results. The coefficients for AggDensity in first two columns of Table 4 are all positive (0.0142 and 0.0225) and statistically significant (p < 0.05 and 0.001), indicating that agglomeration density is
Discussions and implications
This study develops an empirical model of how online consumer reviews change the influence of firm agglomeration on firm revenue in the context of the hotel industry. Based on 11,528 observations of 467 Texas hotels consisting of tax data and online reviews, we analyze the dynamic panel model using a GMM approach. Our empirical results highlight four critical findings. First, consistent with the study of Chung and Kalnins [7], we confirm that firms' agglomeration density positively affects
Declaration of interest
None.
Acknowledgements
This work was supported by the [National Natural Science Foundation of China] under Grant [72032006, 71722014, 71771182, 72011540408 and 72132008]. We also appreciate the Youth Innovation Team of Shaanxi Universities “Big data and Business Intelligent Innovation Team.”
Shan Liu is a Professor and Vice Dean at School of Management in Xi'an Jiaotong University. His research interests focus on business analytics and intelligence, IT-enabled supply chain management, IT project management, and healthcare informatics. He has published more than 60 refereed publications including papers that have appeared in Decision Support Systems, Journal of Operations Management, Information Systems Journal, European Journal of Information Systems, European Journal of
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Shan Liu is a Professor and Vice Dean at School of Management in Xi'an Jiaotong University. His research interests focus on business analytics and intelligence, IT-enabled supply chain management, IT project management, and healthcare informatics. He has published more than 60 refereed publications including papers that have appeared in Decision Support Systems, Journal of Operations Management, Information Systems Journal, European Journal of Information Systems, European Journal of Operational Research, Information & Management, and IEEE Transactions on Engineering Management.
Kezhen Wei is a PhD student at School of Management in Xi'an Jiaotong University. Her research interests focus on business analytics in social media and tourism management.
Baojun Gao is a Professor of Management Science at Wuhan University. He received his BE, MSc and PhD from Xian Jiaotong University. His research interests are in the areas of business analytics and social media. His research has appeared in Tourism Management, International Journal of Hospitality Management, Decision Support Systems, Information & Management, IEEE Transactions on Engineering Management, IEEE Transactions on SMC: Systems, Electronic Commerce Research and Applications, China Economic Review, and Service Science.