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We suggest that the entire world may not always be the appropriate frame of reference in analyses of Multinational Enterprises (MNEs) location choices. In some industries and activities, more narrowly defined geographic areas, such as regions and cities, are more relevant level of analyses. Employing global cities as the geographic frame of reference, we extend the theory of the location choices of MNEs by challenging the assumption that location attributes have identical values for all MNEs. Rather, we explicitly acknowledge the relative value of such attributes for individual MNEs, and search for the firm-specific characteristics that affect this variation. The empirical testing is based on analysis of 673 financial and professional service MNEs that entered New York and London via mergers and acquisitions (M&As). The findings confirm that it is the interaction between location and firm-specific attributes, rather than each of these independently, which affects location choices.

Logistical facility location decisions can make a crucial difference in the success or failure of a company. Geographical Information Systems (GIS) have recently become a very popular decision support system to help deal with facility location problems. However, until recently, GIS methodologies have not been fully embraced as a way to deal with new facility location problems in business logistics. This research makes a framework for categorizing empirical facility location problems based on the intensity of the involvement of GIS methodologies in decision making. This framework was built by analyzing facility location models and GIS methodologies. The research results revealed the depth of the embracement of GIS methodologies in logistics for determining new facility location decisions. In the new facility location decisions, spatial data inputs are almost always coupled with the visualization of the problems and solutions. However, the usage of GIS capability solely (i.e. suitability analysis) for problem solving has not been embraced at the same level. In most cases, the suitability analysis is used together with special optimization models for choosing among the multiple alternatives.

Agricultural sector not only meets the nutritional requirements of all living creatures but also generates the primary source of the raw material provided by various branches of industry to fulfill their functions. It is of great importance to increase studies on oilseeds which have an important role in Turkey's agricultural products. They are grown in almost all of the country, which are vital for the nutrition and many sectors. The main purpose of the study is to offer an integrated approach to determine potential warehouse locations for oilseeds.

This is the first study that integrates Delphi, analytical hierarchical process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), P-Median and Panel data analysis in a real case. This integrated approach consists of the following steps, respectively: (1) The criteria were determined by the Delphi method. (2) The weights of the criteria were calculated by AHP and the provinces with the highest oilseed warehouse potential in seven regions of Turkey were specified by TOPSIS. (3) Oilseed warehouse numbers and locations were obtained by P-Median. (4) In order to answer whether the distribution network is profitable in the future with the determined center locations, a forecast model based on panel data analysis was created. (5) Regional representatives were determined for 2030, and the distribution network was analyzed again. (6) The costs that arose in 2018 and 2030 were computed and compared by cost analysis. (7) The effect of the change in criteria weights on the alternative results was tested by scenario analysis.

The findings indicated that oilseed crop production potential and oilseed crop production area turned out to be the most important criteria. Furthermore, the results showed that this model is robust and suitable for warehouse location selection studies.

The study can serve as a guide for local and central policy makers with both the criteria it uses and the model it develops.

The main contribution of this study is that the integrated approach has been used for the first time in location selection in a real case.

Which retail services are co-located in space? Is it possible to categorize retail stores of different kinds with respect to their location pattern? Acknowledging the spatial dependency between different and similar kinds of retailing activities, the aim of this paper is to find if and to what extent co-location is present in a retail market and what kind of retailing activities are co-located.

The authors analyse the co-location of different types of retail stores in Sweden by using geo-coded data. The data they use allows them to pinpoint each establishment in Sweden down to a 250 by 250m square in space. First, they identify a measure of co-location for each retail service by utilizing pairwise correlations between the different retail service establishments with respect to the squares in which they are present. Later, by using the finest level of industrial categorization for all physical retailing activities (and limiting their geographical unit to the Stockholm metropolitan market), they perform factor analysis to nest these retailing activities under relevant categories based on their co-location pattern.

In this analysis the authors obtain four major factors for the squares with retail stores, in which several kinds of retail activities are nested. These factors reveal a certain degree of location commonality for the markets in question.

The authors' empirical design is based on a highly disaggregated spatial information and the methodology is novel in a sense that it has not been used to address a similar question. Rather than sampling, the authors use the total population, where they take all physical retailing activities into account to be able to draw a general picture for the co-location phenomena in the entire retail market.

The purpose of this paper is to investigate whether new store locations by two incumbent supermarket chains in Singapore were consistent with a preemptive strategy.

The methodology involved collecting store location data and using a geographical technique (Thiessen polygons) for inferring the existence of a preemptive strategy.

The analyses revealed that while NTUC Fairprice’s (the dominant incumbent) new store location strategy was consistent with a preemptive strategy, the second ranked player’s (Cold Storage) was not. Being the dominant incumbent, NTUC Fairprice had the incentive (protect its dominant position) as well as ability (scale, low costs and a merchandise mix that appealed to the mass market) to adopt a preemptive strategy. Cold Storage, on the other hand, lacked both the incentive (appeal to the narrow expatriate segment) and the ability (scale or low costs) to follow a preemptive location strategy.

The analyses did not distinguish between the opening of large vs small stores because the data on store sizes were not available. The analyses focused on an earlier time period because the implementation of the analytical technique (construction of polygons) was more feasible during the timeframe.

The paper identifies conditions under which it may be appropriate for firms to follow a preemptive strategy.

The paper adopts a highly appropriate methodology (Thiessen polygons) that takes into account the locations of own as well as competitor’s stores for analyzing the new store locations by supermarket chains. The paper’s conclusions about the conditions under which preemptive strategies are likely to be adopted can be useful to future researchers as well as managers.

This paper aims to study the determinants of hotel performance, especially the role of location, in the Spanish hotel market.

The sample is composed of 1,034 hotels located in 97 tourist destinations in Spain during the period 2005-2011. The estimations were made by generalised least squares using panel data.

Overall, the results show that hotel attributes are the main determinant of performance. In particular, there is a minimum efficient scale in the hotel business. Location is the second most important determinant. This paper confirms that geographical location models, agglomeration models and competition models are relevant in the study of the effect of location on hotel performance. Regarding management practices, the performance is positively affected by good asset management.

Hotel managers can improve the total net revenue per available room by individually making decisions regarding its characteristics and management practices, especially size and asset efficiency. Moreover, they can collaborate with others (managers and policymakers) to manage tourist destination factors, particularly, demand level, accessibility, negative externalities and market concentration.

This research includes hotel characteristics, management practices and location as determinants of performance, by providing a broader framework of analysis than in previous studies. Regarding location, the empirical analysis considers simultaneously geographical location models, agglomeration models and competition models. The paper studies the Spanish hotel market, which is very important worldwide and which has heterogeneous tourist destinations, thereby making it a good context to analyse the relationship between location and performance.

Blood availability is critical for saving lives in various healthcare services. Ensuring blood availability can only be achieved through efficient management of the blood supply chain (BSC). A key component of the BSC is bloodmobiles, which are responsible for a significant portion of blood donation collections. The most crucial factor affecting the efficacy of bloodmobiles is their location selection. Therefore, detailed decision analyses are essential for the location selection of bloodmobiles. This study proposes a comprehensive approach to bloodmobile location selection for resilient BSCs.

This study provides a novel integration of the spherical fuzzy analytical hierarchy process (SF-AHP) and spherical fuzzy complex proportional assessment (SF-COPRAS) methodologies. In this framework, the criteria are weighted using SF-AHP. The alternatives are then evaluated using SF-COPRAS, employing criteria weights obtained from SF-AHP without defuzzification.

The results show that supply conditions and resilience are the most important criteria for a bloodmobile location selection. Additionally, the validation analyses confirm the stability of the solution.

This study presents several managerial implications that can aid mid-level managers in the BSC during the decision-making process for bloodmobile location selection. The critical factors revealed, along with their importance in choosing bloodmobile locations, serve as a comprehensive guide. Additionally, the framework proposed in this study offers decision-makers (DMs) an effective method for ranking potential bloodmobile locations.

This study presents the first application of multi-criteria decision-making (MCDM) for bloodmobile location selection. In this manner, several aspects of bloodmobile location selection are considered for the first time in the existing literature. Furthermore, from the methodological aspect, this study provides a novel SF-AHP-integrated SF-COPRAS methodology.

With the proliferation of e-commerce companies, express delivery companies must increasingly maintain the efficient expansion of their networks in accordance with growing demands and lower margins in a highly uncertain environment. This paper provides a framework for leveraging demand data to determine sustainable network expansion to fulfill the increasing needs of startups in the express delivery industry.

While the literature points out several hub assignment methods, the authors propose an alternative spherical-clustering algorithm for densely urbanized population environments to strengthen the accuracy and robustness of current models. The authors complement this approach with straightforward mathematical optimization and simulation models to generate and test designs that effectively align environmentally sustainable solutions.

To examine the effects of different degrees of demand variability, the authors analyzed this approach's performance by solving a real-world case study from an express delivery company's primary market. The authors structured a four-stage implementation framework to facilitate practitioners applying the proposed model.

Previous investigations explored driving distances on a spherical surface for facility location. The work considers densely urbanized population and traffic data to simultaneously capture demand patterns and other road dynamics. The inclusion of different population densities and sustainability data in current models is lacking; this paper bridges this gap by posing a novel framework that increases the accuracy of spherical-clustering methods.

This article constructs a distribution’s location model, from the perspective of a firm’s customers, suppliers, and employees, by applying a systematic quality function deployment (QFD) approach. The proposed approach aims to assist a distribution company’s location decision in selecting an optimal location that satisfies the overall location requirements. The QFD procedure began by collecting possible candidate location requirements, followed by conducting the first stage of a sampling survey to identify the secondary location requirements. These were then sorted into major categories of location requirements. Then, the location evaluating criteria were derived from the location requirements and a central relationship matrix was established to display the degree of relationship between each pair of location requirement and location evaluating criterion. Furthermore, the second stage of a sampling survey was conducted to collect data for computing the importance weighting for each category of location requirement. During transformation of the QFD, the importance degree and the normalized importance degree of each location criterion were computed, respectively. The normalized importance degree was, finally, used as the evaluating weight in a distribution company’s location model for the analysis of location evaluation. An empirical study regarding the location decision for a distribution center in Taiwan was provided to demonstrate the proposed approach.