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What drives consumers to use local online retail platforms?

Local online retail platforms (LORPs) are gaining popularity as digital channels that can increase physical retail agglomerations’ attractiveness and viability by stimulating online sales and consumer footfall. However, insights are needed to enrich academic understanding and guide practitioners in their decision-making process regarding use and optimization of these platforms for boosting retail agglomeration vitality. Drawing on uses and gratifications theory, an online survey of 442 Dutch consumers revealed that positive attitudes toward browsing LORPs induced both online purchase and offline visit intentions. Interestingly, despite LORPs' local focus, non-place-specific motives more substantially impacted positive browsing-related attitudes toward LORPs than place-specific ones.

Strategic options for retailers for branding private label portfolios

Private Labels have transformed from value purchases into powerful brands. This paper develops a framework based on the four strategic dimensions of brand breadth, positioning, segmentation, and relationship with the store brand that retailers can uniquely draw upon to organise their brand portfolios. It examines the case of German retailer Rewe that successfully organises its private label portfolio along these dimensions. This paper argues that maintaining multi-tiered and multi-segmented private label portfolios can be important tools for retailers enabling them to cover broader markets, fulfil current consumer needs, build brand equity, and strengthen customer loyalty.

What drives business performance of small and medium-sized enterprises in cross-border electronic commerce?

In cross-border e-commerce, electronic retailers (e-retailers) aim to extend their sales activities via the Internet beyond national borders. Unlike large companies, small and medium-sized enterprises are struggling with their international online expansion. This phenomenon is not limited to countries with a developed e-commerce market; e-retailers from emerging e-commerce markets, too, face the problem. This study investigates what the drivers are of business performance of SMEs in cross-border e-retailing, and how drivers differ between developed and emerging e-commerce markets in Europe. Structural equation modelling analyses with the lavaan package in R on a sample of 453 owners and directors of SMEs from 20 countries, show that foreign market orientation not only directly influences business performance in cross-border e-commerce, but also indirectly through communication efforts in foreign markets. These results hold for both developed and emerging markets, however, there are two interesting differences. First in the influence of foreign market orientation and communication efforts on business performance, and second in the impact of the number of years the electronic e-retailer is active in cross-border e-commerce.

Business performance and purchase intentions in cross-border e-commerce

Cross-border e-commerce is flourishing worldwide and is particularly intriguing because it allows sellers and buyers to regularly cross national borders to distant and distinct countries via the Internet. Marketers need to understand the challenges retailers and consumers may face to develop effective marketing strategies, attract foreign consumers to retailers’ websites, and convert their visits into actual purchases. This dissertation contributes to the growing literature on cross-border e-commerce by examining how e-retailers can shape their marketing strategy to reach foreign consumers who may make a purchase and what drives consumers’ perceptions and preferences before making thatpurchase. To this end, study 1 examines how and to what extent small e-retailers can shape their marketing strategies to increase their use of digital marketing tactics and thereby improve their performance in foreign markets by comparing e-retailers originating from developed and emerging e-commerce markets. Study 2 focuses on how store values and country stereotype perceptions leadto higher trust between consumers and retailers in foreign e-stores, and how this differs for European consumers shopping at U.S. and Chinese e-stores. The thirdstudy addresses why consumers buy from foreign e-stores when they can buy domestically. It examines three different categories of determinants across generational cohorts: e-store characteristics, domain-specific values, and human values. Overall, this dissertation demonstrates the drivers of small retailers’ business performance and consumers’ purchase intentions in cross-bordere-commerce while showing that neither e-retailers nor consumers should be considered uniform or generalizable.

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%.

Serendipity and retail