Distribution centres are becoming more and more relevant for spatial planning, due to their rapidly increasing size and number. There is little literature, however, that provides a generalized analysis of the size and functional attributes of distribution centres, and none that discusses the relationships between these attributes. Our aim is to fill this gap by providing new evidence and analysis to understand this relationship. We make use of an extensive database of 2888 DCs in the Netherlands to develop a new typology of DCs based on the geographical location of DCs, their functional attributes and client sector characteristics. The analysis shows that the context in which medium sized DCs are operating is more heterogeneous than in the case of very large and small size DCs. This study is a first attempt to analyse this relationship between facility size and functions based on a rich and extensive dataset of large population of DCs. The results can serve as input for further quantitative statistical analysis and international comparison.
Retail industry consists of the establishment of selling consumer goods (i.e. technology, pharmaceuticals, food and beverages, apparels and accessories, home improvement etc.) and services (i.e. specialty and movies) to customers through multiple channels of distribution including both the traditional brickand-mortar and online retailing. Managing corporate reputation of retail companies is crucial as it has many advantages, for instance, it has been proven to impact generated revenues (Wang et al., 2016). But, in order to be able to manage corporate reputation, one has to be able to measure it, or, nowadays even better, listen to relevant social signals that are out there on the public web. One of the most extensive and widely used frameworks for measuring corporate reputation is through conducting elaborated surveys with respective stakeholders (Fombrun et al., 2015). This approach is valuable but deemed to be laborious and resource-heavy and will not allow to generate automatic alerts and quick and live insights that are extremely needed in this era of internet. For these purposes a social listening approach is needed that can be tailored to online data such as consumer reviews as the main data source. Online review datasets are a form of electronic Word-of-Mouth (WOM) that, when a data source is picked that is relevant to retail, commonly contain relevant information about customers’ perceptions regarding products (Pookulangara, 2011) and that are massively available. The algorithm that we have built in our application provides retailers with reputation scores for all variables that are deemed to be relevant to retail in the model of Fombrun et al. (2015). Examples of such variables for products and services are high quality, good value, stands behind, and meets customer needs. We propose a new set of subvariables with which these variables can be operationalized for retail in particular. Scores are being calculated using proportions of positive opinion pairs such as <fast, delivery> or <rude, staff> that have been designed per variable. With these important insights extracted, companies can act accordingly and proceed to improve their corporate reputation. It is important to emphasize that, once the design is complete and implemented, all processing can be performed completely automatic and unsupervised. The application makes use of a state of the art aspect-based sentiment analysis (ABSA) framework because of ABSA’s ability to generate sentiment scores for all relevant variables and aspects. Since most online data is in open form and we deliberately want to avoid labelling any data by human experts, the unsupervised aspectator algorithm has been picked. It employs a lexicon to calculate sentiment scores and uses syntactic dependency paths to discover candidate aspects (Bancken et al., 2014). We have applied our approach to a large number of online review datasets that we sampled from a list of 50 top global retailers according to National Retail Federation (2020), including both offline and online operation, and that we scraped from trustpilot, a public website that is well-known to retailers. The algorithm has carefully been evaluated by manually annotating a randomly sampled subset of the datasets for validation purposes by two independent annotators. The Kappa’s score on this subset was 80%.
MULTIFILE
This study proposes a framework to measure touristification of consumption spaces, consisting of concentration of retail capital, business displacement and standardization of the consumption landscape. This framework is tested using business registration data and rent price estimates for consumption spaces in Amsterdam between 2005 and 2020. Touristification emerges from concentrations of retail capital and standardization, but occurs without causing significant business displacement. A cluster analysis identifies different variations of touristification. Besides the more typical cases these include nightlife areas, gentrifying consumption spaces and specialized retail areas. This suggests that local contingencies cause consumption spaces to respond differently to increasing tourism.
Supermarkets are essential urban household amenities, providing daily products, and for their social role in communities. Contrary to many other countries, including nearby ones, the Netherlands have a balanced distribution of supermarkets across villages and urban neighbourhoods. However, spatial supermarket patterns, are subject to influential developments. First, due to economies of scale, there is a tendency for supermarkets to increase their catchment areas and to disappear from peripheral villages. Second, supermarkets are now mainly located in residential areas, although the urban periphery appears to be attractive for the retail sector, perhaps including the rise of hypermarkets. Third, today, online grocery shopping is still lagging far behind on other online shopping products, but a breaks through will dilute population support for in-store supermarkets and can lead to dramatic ‘game changer’ shifts with major spatial and social effects. These three important trends will reinforce each other. Consequences are of natural community meeting places at the expense of social cohesion; reduced accessibility for daily products, leading to more travel, often by car; increasing delivery flows; real estate vacancies, and increasing suburban demand increase for retail and logistics. Expected changes in supermarket patterns require understanding, but academic literature on OGS is still scarce, and does hardly address household behaviour in changing spatial constellations. We develop likely spatial supermarket patterns, and model the consequences for travel demand, social cohesion and real estate demand, as well as the distribution between online and in-store grocery shopping, by developing a stated preference experiment, among Dutch households.
