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China is the world’s largest user market for digital technologies and experiencing unprecedented rates of rural-urban migration set to create the world’s first “urban billion”. This is an important context for studying nuanced adoption behaviours that define a digital divide. Large-scale studies are required to determine what behaviours exist in such populations, but can offer limited ability to draw inferences about why. The purpose of this paper is to report a large-scale study inside China that probes a nuanced “digital divide” behaviour: consumer demographics indicating ability to pay by electronic means but behaviour suggesting lack of willingness to do so, and extends current demographics to help explain this.

The authors report trans-national access to commercial “Big Data” inside China capturing the demographics and consumption of millions of consumers across a wide range of physical and digital market channels. Focusing on one urban location we combine traditional demographics with a new measure that reflecting migration: “Distance from Home”, and use data-mining techniques to develop a model that predicts use behaviour.

Use behaviour is predictable. Most use is explained by value of the transaction. “Distance from Home” is more predictive of technology use than traditional demographics.

Results suggest traditional demographics are insufficient to explain “why” use/non-use occurs and hence an insufficient basis to formulate and target government policy.

The authors understand this to be the first large-scale trans-national study of use/non-use of digital channels within China, and the first study of the impact of distance on ICT adoption.

This paper aims to clear whether the monopoly structure of the internet industry has produced market power and discussed the welfare change of the internet industry monopoly.

By using new empirical industrial organization methods and taking the e-commerce market as an example, the authors measured market power and economies of scale of the internet platform companies.

Internet platform enterprises have formed scale economy, but it has not had market power, and the industry still maintains high levels of competition; also, the emergence of large enterprises may increase the welfare of consumers.

The conclusion of this paper clarified actual competition status of internet industry and provided a new foothold for regulation and ideas for the traditional industry to crack the Marshall Conflict.

Drawing from legitimacy and institutional entrepreneurship theory, this study assesses the naming patterns of entrepreneurial firms in the US biotechnology industry.

The authors use a mixed-methods design of content analysis and regression to analyze a sample of 441 entrepreneurial biotechnology firms, for which data were obtained from Net Advantage. The authors track changes to the proportion of firms with naming attributes, such as name length and type of name. The authors also examine variability in those characteristics during the industry's evolution, comparing freestanding to acquired start-ups.

Start-ups select names that are longer, more descriptive, begin with rare sounds or hard plosives and have stronger discipline- or technology-specific links during nascent years of the industry. As the industry evolves, entrepreneurs are more likely to select names that are shorter, more abstract, begin with hard plosives and have stronger industry-specific links. The naming patterns of freestanding and acquired companies differ, and companies that conform to industry pressures tend to remain independent.

Unlike extant studies that assess established industries, the current study identifies shifting trends in the naming patterns of entrepreneurial firms in an emerging industry. By focusing on start-ups, the authors expand research on organizational naming practices, which focuses traditionally on name choices and name change patterns of incumbents. By using marketing and linguistics methods when analyzing organizational name attributes, naming patterns in these attributes are identified, including name length, name type, starting letter of the name and link to the industry.