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Serendipity and retail

RFID in Retail: New approaches, new viewpoints

RFID technology is a promising technology currently finding its way into the field of customer interaction strategy, supply chain accuracy and inventory management. Primarily, RFID tags are seen as substitutes of traditional barcodes, yet they can add a lot of value and functionality. Where barcodes require a scanning device to be placed directly in front of the tag to be read, RFID tag readers are able to scan all tags that are in the proximity of the scanner . The next difference is that whereas barcodes usually are the same for all articles of the of the same type (i.e. a jar of peanut butter of brand x), RFID tags will be unique for each individual product occurrence. This opens up the possibility of tracking the entire history of a specific occurrence of a product. Moreover, due to the nature of the scanning technology, it suddenly becomes achievable for manufacturers to track individual products through all stages of production and base inventory management and front office planning on real-time data at item level from production facilities.

Geographies of touristification of consumption spaces

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.

Automatic Measurement of Corporate Reputation for Retail Companies from Online Public Data on the Web

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%.

The impact of retail strategies, utilitarian benefits, shopping and holiday habits on consumer’s food waste behavior.

Reducing food waste at the consumer level is a critical component of the U.N. sustainable development goals, yet consumer behavior in this domain remains complex and multifaceted. This study extends the Theory of Planned Behavior (TPB) to develop a comprehensive model of food waste behavior in a developed economy, integrating both cognitive and structural factors that influence consumer decisions. Using survey data from 309 U.S. households, we empirically test the impact of retail strategies, perceived utilitarian food waste benefits, and holiday habits on food waste behavior. The findings reveal that retail strategies, such as promotions and packaging tactics, encourage over-purchasing, contributing to increased waste. Additionally, consumers who perceive benefits in discarding food–such as making space for fresher items or avoiding the effort of meal planning–exhibit higher food waste tendencies. Seasonal consumption patterns further amplify waste, as holiday-related shopping habits drive excessive food acquisition. These results underscore the need for interventions that address both individual decision-making processes and broader market-driven influences. By integrating structural and habitual determinants into the TPB framework, this study enhances the theoretical understanding of food waste behavior and offers practical implications for policymakers and retailers seeking to reduce consumer-level food waste.

Moving towards explorative space as a strategy for survival.