Our paper investigates the microfoundations of sustainable entrepreneurship and aims to shed light on trade-offs made in decisions about social, ecological and economic sustainability. Balancing the three dimensions of sustainability (social, ecological and economic) inherently requires choices in which one dimension or another has less optimal outcomes. There is not much known about the rationale that sustainable entrepreneurs use for making such trade-offs. Thus, we ask how does entrepreneurial orientation affect decisions and trade-offs on sustainability impact? Our study is an exploratory, qualitative study of 24 sustainable entrepreneurs. We collected data about entrepreneurial orientation and sustainability trade-offs and held in-depth interviews with a subsample of six firms. We conducted a cluster analysis based on four entrepreneurial orientations (innovativeness, proactiveness, riskiness and futurity) and three sustainability trade-off dimensions (environmental, social and economic). From the findings, we derive a typology of three types of sustainable entrepreneurs: green-conflicted, humanitarian-oriented and holistically-oriented. We uncover salient characteristics and aspects of entrepreneurial orientation in relation to trade-off decisions. We find that the entrepreneurs accept slower economic growth or lower performance in order to maintain the integrity of their social and ecological principles and values.
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%.
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An interactive full-length mirror that allows you to browse through an endless collection ofclothing and see immediately whether something fits you, including when you turn around, and which also allows you to send a picture quickly to your family and friends to hear what they think. This mirror is a technological development that is already possible and which is being introduced in fashion stores here and there. But how probable is it that this technological innovation will become a permanent feature of our shopping experience? To answer this question we shall describe the expectations that exist about the developments in shopping over the coming years. We shall then examine to what extent these developments already play a role in shopping now, in 2014. In order to maintain an overview, we shall introduce a typology based on the STOF model. All of the innovations mentioned are ultimately aimed at offering added value for the consumer, but who is that consumer and what does he or she need? An inventory of how the shopping consumer is regarded makes it clear that new perspectives are required in order to do justice to the complexity of the retail behaviour and the retail experience. Finally, we will briefly examine specific cross-media aspects of shopping, such as the multichannel strategy of retail outlets and the role of the physical store in relation to the webshop. We end by offering a research framework for the 'service encounter' in the retail process based on the concept of Servicescapes. This framework allows to chart and answer a number of essential questions surrounding the probability of innovations more systematically.
De retailsector verandert diepgaand en structureel. Door ontwikkelingen in technologie, sociaal-culturele en demografische trends en ook door veranderingen binnen het domein van retail zelf, staan veel ondernemingen en andere stakeholders, zoals gemeenten, de vastgoedsector en toeleveranciers van het winkelbedrijf voor belangrijke uitdagingen. Dit veld vormt een belangrijk onderzoeksthema van praktijkgericht onderzoek van lectoraten binnen het Hoger Beroepsonderwijs. Dat is nu nog versnipperd, maar kan en kracht en relevantie winnen bij samenwerking. Het lectorenplatform Retail Innovation beoogt door middel van bundeling en gecoördineerde en deels gezamenlijke uitvoering van nieuw praktijkgericht retail onderzoek door de lectoren in het HBO een betere gestructureerde bijdrage leveren aan de noodzakelijke innovatie in de retailsector in Nederland, in het bijzonder op basis van de nationale retail(onderzoeks)agenda. Vanuit die bundeling de verbinding leggen met de retailsector, samen met geassocieerde partners zoals TKI CLICKNL, belangstellende universiteiten en relevante organisaties en vertegenwoordigers van de retailsector om de doorwerking van bevindingen en resultaten verder te versterken.
