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The aim of this paper is twofold. First, clustering patterns of urban hotels are explored, and, second, clustering effects on performance for upscale urban hotels are estimated.

Local indicators of spatial association (LISA) were computed using geographic information system (GIS) techniques. Clustering for the entire population of hotels in Madrid was explored visualizing LISA statistics. Then, a system generalized method of moments regression was applied to test a set of hypotheses about the performance effects of LISA statistics for a sample of upscale urban hotels.

Two significantly distinct types of clusters are identified: dense “cold spots” or clusters containing many low-priced hotels and quiet “hot spots” or clusters only containing a few high-priced hotels. And, estimates confirmed two important results: evidence of adverse selection when clustering and evidence of positive location economies for upscale hotels.

This study has a number of relevant implications for making better hotel location decisions. Specifically, the paper shows the applicability of GIS to find statistically significant clustering in the data. In the hotel sector, knowing exactly where hotel clustering occurs and of what type is of vital importance.

This paper’s novel application of LISA based on GIS techniques for hotel clustering sheds light on the effects of clustering on performance to convey the subtle nuances of the relationship for upscale urban hotels.

The purpose of the paper is to verify whether the version of neighbourhoods created from the lowest geographical level improve a predictive accuracy of hedonic model in comparison with those based on upper geographical levels.

The paper proposes a method for defining neighbourhoods using Thiessen polygons. The clustering technique is based on fuzzy equality. Clustering is started at different geographical levels: municipalities, traffic analysis zones, and apartment blocks' Thiessen polygons. Delineated neighbourhoods are incorporated into hedonic model of apartment prices, the applied methodologies are ordinary least squares and spatial error.

With ordinary least squares regression, the slight superiority of Thiessen polygons is found in both in‐sample analysis and ex‐sample prediction. With spatial error technique, the clusters of Thiessen polygons do not always provide the best outcome, and their superiority is contested by the highest geographical level of municipalities.

This paper is the first attempt to apply the proposed method, which not always demonstrates clear superiority. In future study, the method of neighbourhood delineation could be used in combination with market segmentation.

The proposal to use Thiessen polygons as a transition from points to continuous space can outline a base for the use of different clustering techniques, which are applicable to delineate neighbourhoods in housing market studies, in particular for the assessment purpose. The fuzzy equality clustering algorithm itself can be applied to polygonal data.

The originality of the proposed method is that it defines neighbourhoods starting from individual observations applying fuzzy equality. Its advantages are an increased independence from existing boundaries, self‐determination of a number of clusters, and total coverage of an area.

To test the applicability of the user-generated content (UGC) derived from social travel network sites for online reputation management, the purpose of this study is to analyze the spatial clustering of the reputable hotels (based on the TripAdvisor Best-Value indicator) and reputable outdoor seating restaurants (based on ranking indicator).

This study used data mining techniques to obtain the UGC from TripAdvisor. The Hierarchical Density-Based Spatial Clustering method based on algorithm (HDBSCAN) was used for robust cluster analysis.

The findings of this study revealed that best value (BV) hotels and reputable outdoor seating restaurants are most likely to be located in and around the central districts of the urban tourist destinations where population and economic activities are denser. BV hotels' spatiotemporal cluster analysis formed clusters of different sizes, densities and shape patterns.

This study showed that reputable hotels and restaurants (H&Rs) are concentrated within districts near historic city centers. This should be an impetus for applied research on urban investment environments.

The findings would be rational guidance for entrepreneurs and potential investors on the most attractive tourism investment environments.

There has been a lack of studies focusing on analyzing the spatial clustering of the H&Rs using UGC. Therefore, to the best of the authors’ knowledge, this study is the first to map and analyze the spatiotemporal clustering patterns of reputable hotels (TripAdvisor BV indicator) and restaurants (ranking indicator). As such, this study makes a significant methodological contribution to urban tourism research by showing pattern change in H&Rs clustering using data mining and the HDBSCAN algorithm.

为了测试社交旅游网站 (STNS) 的用户生成内容 (UGC) 对在线声誉管理 (ORM) 的适用性, 本研究分析了知名酒店的空间聚类（基于 TripAdvisor 最佳价值指标) 和信誉良好的户外座位 (ODS) 餐厅(基于排名指标)。

该研究使用数据挖掘技术从 TripAdvisor 获取 UGC。 基于（HDBSCAN）算法的分层基于密度的空间聚类方法用于鲁棒聚类分析。

调查结果显示, 最具价值 (BV) 酒店和信誉良好的 ODS 餐厅最有可能位于人口和经济活动较为密集的城市旅游目的地的中心区及其周边地区。 BV 酒店的时空聚类分析形成了不同大小、密度和形状模式的聚类。

目前的文献扔缺乏专注于分析利用 UGC 的酒店和餐厅 (H&R) 空间聚类的研究。 因此, 本研究首次绘制并分析了知名酒店（TripAdvisor BV 指标）和餐厅（排名指标）的时空聚类模式。 因此, 本研究通过利用数据挖掘和 HDBSCAN 算法显示 H&Rs 聚类的模式变化, 为城市旅游研究做出了重要的方法论贡献。

这项研究表明, 著名的 H&R 集中在历史悠久的市中心附近的地区。 这应该是对城市投资环境的应用研究的推动力。

研究结果将为企业家和潜在投资者提供最具吸引力的旅游投资环境的理性指导。

The purpose of this paper is to explore why and how firms with logistics-intensive operations such as fast-moving consumer good (FMCG) distributors benefit from residing in logistics clusters. In particular, this study seeks to fill a gap in the understanding of how logistics clustering may influence FMCG firms’ distribution capabilities.

Three case studies of FMCG distributors geographically agglomerated within Q Logistics Cluster in Jordan serve to elaborate the existing theory of clustering. Data were collected from 24 interviews as well as observational evidence of the FMCG distributors’ outbound logistics operations. The unit of analysis was the interaction between FMCG distributors and other agents in the logistics cluster.

FMCG distributors tend to gravitate to clusters where logistics service providers and other FMCG firms co-locate. FMCG distributors interact intensively and benefit greatly from building ties with non-competitor distributors in a cluster. Informal personal relations, collaborative activities and knowledge sharing, learning opportunities and resource availability were found to act as mechanisms for generating distribution capabilities within a logistics cluster.

This study provides practical implications for FMCG logistics and distribution managers who make distribution centre (DC) location decisions. The study provides such managers and their firms with a deeper understanding of the importance of co-locating DCs in logistics clusters, and may help them in designing their supply networks.

This is the first scholarly work to uncover the various ways in which FMCG distributors benefit from logistics clustering and explain why they may differ in performance, building on observations of their capabilities. The study provides insight from an emerging market and encourages future researchers to conduct further studies on logistics clustering in order to bring relevant theory forward.

This paper aims to explore a new model to manage small and medium enterprise (SME) clustering process that examines the geographical connectivity conditions within the existing theories on agglomeration. The presented work explores the dynamics governing the decisions related to both the duration and frequency of the different forms of these new clusters.

A clustering configurator tool is developed to assist managers for the best temporary cluster model. The configurator considers aspects related to the market, industry and classical clustering requirements as well as social capital (SC). Finally, the performance of various temporary clusters under different demand scenarios and operational conditions are studied using numerical simulation.

The results examined the performance of the new clusters under various internal and external defining indicators against potential economic growth, technology spillover and the new metric of SC. The results offered interesting observations suggesting various recommendations to promote these new models to SMEs as well as how to better manage them.

The presented results are understood in the context of the suggested settings of relationships and scoring weights.

The new form of clusters will help SMEs overcome the feasibility challenge when considering re-locating to existing clusters while reaping many of these clusters benefits. Furthermore, different recommendations for management aimed at enhancing clustering decisions and the efficiency of SMEs in these new setups are presented.

This paper suggests a new clustering management approach that capitalizes on the temporal domain rather than classical space or the digital clusters domains. Also, a new management concept called dynamic matching is suggested. SC is considered among clustering objectives which was disregarded in similar studies.

The purpose of this paper is to examine the role of geographic location of research-intensive firms in the ability to generate new research and products, which consequently affects firm value.

The authors conduct the empirical study following a three-step process. First, if pharmaceutical firms are more likely to cite the patents of other firms and other innovators that are nearby, as opposed to firms and other innovators that are far away, then location (i.e. close proximity) is likely important when it comes to the ability to learn and to use the knowledge being generated by other innovators. The authors employ a “geographic information systems” (GIS) and geo-code each pair of citing and cited patents. In addition, the authors utilize spatial statistics such as Moran’s I to analyze the spatial clustering pattern of patent citations and knowledge flows. Next, the authors measure the pharmaceutical companies’ ability to generate useful patents as a function of the amount of innovation and industrial activity that is occurring close to them. Finally, the authors test whether a firm’s location relates to its firm value. Specifically, the authors model firm value as a function of its patents quality, but the authors also allow the firm’s patents quality to be a function of its location and locational attributes. In this way, the authors establish a link between location and firm value. Using a simultaneous system of equations, the authors find that location explains patent quality, which, in turn, explains firm value. In other words, there is a positive relationship between firm value, innovation and location.

In empirical tests using pharmaceutical firms and their patents, the authors first find that firms more often cite patents of other firms that are geographically closer to them than those firms that are farther away. The authors then find that a patent’s quality is a function of the firm’s near proximity to other knowledge-intensive institutions and activities. Finally, the authors find that because patent quality is a function of a firm’s geographic location, location consequently affects firm value.

For knowledge-intensive firms, geographic location matters. More specifically, the authors contend that research-intensive firms are better able to use and to expand on existing knowledge when they are closer to other research-intensive enterprises. The implication is that firm value maximization involves a location factor.

The practical implication for investors is that investors should invest in those firms that are situated in a location that is rich in geographic innovation resources because those firms are more likely to generate more and higher quality patents or innovations.

The study is the first to establish the linkage among spatial knowledge diffusion, geographic drivers of innovation, and market valuation of the firm. The study is unique in that the authors not only present evidence on spatial knowledge flows by geo-coding the exact longitude and latitude location coordinates of citing and cited patens, but more importantly, the authors also identify geographic drivers of innovation, and examine their impacts on citation-weighted patent counts and knowledge stock. Finally, using a series of simultaneous equations, the authors show how geographic innovation resources positively affect citation-weighted patent stock and knowledge stock and consequently affect market value of the firm. Thus, the novel approach contributes not only to the literature that measures geographic localization of knowledge flow using patent citations, but also to the literature that examines the impact of geographic sources of innovations on patent outputs and patent quality and, thus on firm value for research-intensive firms.

This study aims to explore how the fact of belonging to clusters of dissimilar form or characteristics modify the application of human resource management (HRM) practices, as well as those knowledge-sharing processes that guide and encourage the intrapreneurial behavior of employees (IPB) in firms belonging to the cluster. The main thesis is that the application of HRM practices and some knowledge management processes are strongly conditioned by the form or characteristics of the cluster, all this in a knowledge-intensive context that requires a contingent application of such practices.

The research strategy chosen was a qualitative case study, given that the insight the authors were seeking could only be obtained through a fine-grained analysis inside the firm where it is very difficult to decouple the phenomenon to be observed from the context where it takes place. Two cases were selected to analyze the phenomenon in-depth and compare their results; they were big and technologically advanced firms but belonging to clusters of different forms and characteristics.

Results show that the influence of the cluster based on location is greater than the effects of the cluster formed by networks, where globalization and external ties play an important role. HRM practices and knowledge sharing processes that lead to intrapreneurial behavior are conditioned, only in part, by the characteristics of the cluster. Particularly, the geographical cluster encourages knowledge sharing with competitors and customers, mainly for technical training processes and because of belonging to a sectoral association. However, HRM practices, with the exception of training and compensation policies, are mainly conditioned by the company's culture and internal factors, rather than by belonging to a specific cluster.

Firms belonging to an organized cluster should encourage the development of practical training-oriented programs, not only on technical aspects but also on other skill and competence-based areas. In addition, training based on strategic issues both for top and middle managers could be an interesting initiative. Additionally, clustered firms should develop more knowledge-retention policies to limit the degree of rivalry in the sector, as it is very common for a firm to search for new and specialized talent in the rest of competing firms in the cluster.

Considering the economic impact of the geographical cluster, its effect on the employment and development of a region and taking into account the relevant and dynamic role of research institutions and associations, policymakers should support and facilitate the activity of those institutions, reinforcing the relevance of industrial districts or geographical clusters that are threatened by the pressures of globalization.

This study brings new insight into the effect of the form and characteristics of the cluster on HRM practices and knowledge sharing processes that lead to intrapreneurial behavior. The study may open the field for additional studies that, from a qualitative and quantitative perspective, analyze this topic in depth. The paper shows that IPB depends not only on the support of the institutions created in the cluster but also on the culture and competitive strategy of the company. Belonging to a geographical cluster can have an influence on firms’ behavior and can, through the trust generated among its members, facilitate knowledge-sharing processes and intrapreneurial behavior.

The purpose of this paper is to analyze the benefits of logistics clustering, with the intent to explain their growth and popularity among private agents and policy makers during the last decade.

Because of limited knowledge about the benefits of logistics clustering a main objective of this paper is theory building. The authors employed a grounded theory approach and conducted 135 open and semi structured interviews with logistics executives, government officials, academics, and chambers of commerce representatives.

Study results revealed that, the agglomeration of logistics firms provides several key benefits to companies. Specifically, it was found that logistics clustering facilitates collaboration-related benefits, offering of value added services, career mobility for the logistics workforce within the cluster, and promotes job growth at multiple levels within the cluster. The authors offer a rich description of these benefits and the mechanisms that facilitate these outcomes.

This paper uses a qualitative approach. Further research using a quantitative approach to measure the magnitude of the impacts and benefits of logistics clusters would be desirable.

This paper identifies the benefits associated with logistics clustering and thus provides justification for public authorities and private agents to invest in this phenomenon.

The paper models the effect of spatial clustering on various dimensions of inter-firm competitive rivalry among port logistics firms using Porter's five forces model.

A survey-based quantitative approach is adopted to collect data from logistics firms, which are directly or indirectly dependent on the Port of Melbourne in Australia. A structural equation modelling (SEM) technique is used to examine the theorised relationships between various dimensions of inter-firm competitive rivalry and the tendency of spatial clustering of logistics firms in the vicinity of Port of Melbourne.

The results indicate that the inter-firm competitive rivalry increases significantly when logistics firms are spatially clustered. This effect is further augmented when they cluster around the port. Co-location of firms near the port tends to increase “bargaining power of buyers”, whilst indirectly affecting “competitive rivalry” via “threats of substitutes”. This indicates that co-location enhances the bargaining power of buyers through the greater availability of substitute services that in turn promotes competitive rivalry among firms. However, co-location has an insignificant effect on “barriers to entry” and “bargaining power of suppliers”. Low entry barrier thus favours high competitive rivalry among firms. Hence, this paper validates the Porter's cluster and five forces models that confirm the positive effect of port logistics clusters (PLCs) on bargaining power of buyers and indirect effect on competitive rivalry partially mediated through threats of substitutes.

This study provides empirically grounded evidence for firms to evaluate co-location decision choices and help buyers and sellers to devise business strategies to enhance inter-firm competitive rivalry and bargaining power.

This is the first systematic attempt to empirically validate Porter's five forces model in the context of PLC. Furthermore, the conceptualisation of PLC concept both as spatial and functional constructs (i.e. dependency on port) is novel. This study thus has broadened the meaning of cluster from a geographic entity to a more useful functional construct to reflect inter-firm dependencies.

This study aims to determine how much company participation in a type of cluster affects its economic performance.

This study includes companies operating in seven industries (automotive, engineering, textiles, information technology (IT) services, furniture, packaging and nanotechnology) in the Czech Republic. The companies are divided into three groups: members of institutionalized cluster, operating in the same region (natural clusters) and operating in other regions. Data envelopment window analysis is used to measure their performance for 2009–2019.

Results show that the effect of clustering differs among industries. Companies in three industries (automotive, engineering, nanotechnology) reveal a positive impact of the cluster initiative on performance growth. Two industries (textile, packaging) with companies operating in a natural cluster show better performance than those in an institutionalized cluster. Moreover, the IT services and the furniture industries show no positive effect of clustering on corporate performance.

This research includes 686 companies from seven industries and monitored for 11 years. On the one hand, the sample includes a relatively high number of companies overall; but on the other hand, the sample is relatively small, especially for nonclustered companies. The reason is the lack of available financial statements for small companies.

From the perspective of practical cluster policy, the authors can recommend that monitoring the performance of member companies in clusters must be one of the criteria for evaluating the success of a cluster, such as cluster initiatives.

This study distinguishes between long-standing natural clusters in a given industry and institutionalized ones that have emerged because of a top-down initiative. An original database is created for clustered and nonclustered companies in seven industries, covering the entire Czech Republic.