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Serendipity and retail

From Schedule Push to Reality Pull: Reality Pull prefers Retail

The Internet is changing the way we organise work. It is shifting the requirement for what we call the ‘schedule push’ and the hierarchical organisation that it implies, and therefore it is removing the type of control that is conventionally used to match resources to tasks, and customer demand to supplies and services. Organisational hierarchies have become too expensive to sustain, and in many cases their style of coordination is simply no longer necessary. The cost complexity of the industrial complex starts to outweigh the benefits and the Internet is making it redundant.

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%.

Handleiding EHB(retail) lab

Binnen het project Future-Proof Retail werden acht labformules ontworpen en getest. Het EHBR(etail) lab bleek een van de drie succesformules te zijn: alle betrokken stakeholders hebben deze vorm van samenwerking beoordeeld als heel positief. Tussen 2018 en 2020 vonden zes edities van het lab plaats in verschillende gemeenten in Zuid-Holland. Hierbij had De Haagse Hogeschool de leiding. Onder regie van de opleiding Ondernemerschap & Retail Management werden derde jaarsstudenten via een minor ingezet. De bedoeling van deze handleiding is om te zorgen voor een opschaling van het EHBR(etail) lab in meerdere Nederlandse regio’s en in samenwerking met andere hogescholen en mbo-onderwijsinstellingen. Hierbij is het belangrijk om te realiseren dat de regierol niet alleen specifieke expertise en ervaring in businessmanagement vraagt, maar ook een serieuze investering in tijd en geld. Bovendien is intensieve inzet van hbo-studenten nodig: twee dagen per week gedurende een semester, of minimaal een onderwijsblok van tien weken. Tijdens het living lab worden retailers geactiveerd en kan er een nieuw of aangepast businessmodel ontstaan. Zowel voor individuele retailers of een heel winkelgebied. De verschillende vragen die wor den opgepakt in een EHBR(etail) lab, zijn veel breder dan de vragen bij andere labs. Het inhoudelijke proces is compleet anders dan bij een Hype lab en Lab Circularity, namelijk iteratief - hierbij verwij zen wij graag naar de handleidingen van deze twee labs. Studenten doen met de eerste (hulp)vraag van de individuele retailer of van een heel winkelgebied als startpunt een empathisch onderzoek naar de omgeving. Ze gaan op zoek naar de vraag achter de vraag: wat is nu eigenlijk het probleem of de uitdaging van de ondernemer of het collectief? In het EHBR(etail) lab werken hbo-studenten, bij voorkeur samen met mbo-studenten, met onder zoekers en het bedrijfsleven aan innovatief onderzoek. Dat gebeurt in een zogenaamde quadruple helix-omgeving (zie figuur 1). In cocreatie ontwikkelen de verschillende partijen praktische tools. Gemiddeld nemen vijf tot twintig retailers deel aan een lab. Samen met de studenten doen ze bijvoorbeeld onderzoek naar de relevantie van de deelnemende retailers voor bestaande en nieuwe klanten. De studenten lichten bestaande businessmodellen inclusief ‘customer journey’ door. Het lab werkt in sprints van zes à tien weken, en de deelnemers hanteren de methode van design thinking. Het succes van het lab bleek namelijk in grote mate samen te hangen met de design-thinking skills van de betrokken studenten en docenten. Studenten die in labs het probleem van ondernemers en medewerkers konden herkaderen (het probleem áchter het probleem boven tafel wisten te krijgen), konden veel waarde toevoegen aan het leerproces van de ondernemer. Zeker omdat zij volgens design thinking verbeterplannen ook concreet konden toepassen en uittesten in experimenten.

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.

Future retail city centre