Zoekresultaten

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.

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.

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%.

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.

Restaurant location in Hamilton, New Zealand : clustering patterns from 1996 to 2008

Purpose: The purpose of this study is to assess the evolution of restaurant locations in the city of Hamilton over a 12-year period (1996 to 2008) using GIS techniques. Retail theories such as central place, spatial interaction and principle of minimum differentiation are applied to the restaurant setting. Design/methodology/approach: A database of restaurants was compiled using the NZ yellow pages and contained 981 entries that consisted mainly of location addresses and types of cuisine. This paper focuses on locational patterns only. Findings: A process of geo-coding and clustering enabled the identification of two clustering periods over 12 years for city restaurants, indicating locational patterns of agglomeration within a short walking distance of the CBD and spill over effects to the north of the city. Research limitations/implications: The data do not allow statistical analysis of the variables causing the clustering but offer a visual description of the evolution. Explanations are offered on the possible planning regimes, retail provision and population changes that may explain this evolution. Practical implications: The findings allow identification of land use patterns in Hamilton city and potential areas where new restaurants could be developed. Also, the usefulness of geo-coded data in identifying clustering effects is highlighted. Originality/value: Existing location studies relate mostly to site selection criteria in the retailing industry while few have considered the evolution of restaurant locations in a specific geographic area. This paper offers a case study of Hamilton city and highlights the usefulness of GIS techniques in understanding locational patterns.

Serendipity and retail