This study examines the dynamics of the digital divide between middle- and low-income groups of 44 African countries in the context of three technologies – mobile cellular, Internet, and fixed broadband – from 2000 to 2015. At the macro level, the relative digital divide has been narrowing at the annual rates from 11.3% to 0.72%, while the absolute digital divide has been widening at the annual rates from 31.33% to 17.11%. At the microlevel, convergence analysis indicates that a catch-up process has taken place in both income groups, with the low-income group displaying a faster catch-up speed in all three technologies. Combining the findings from the macro and the microlevel of the analysis revealed a positive relationship between the increasing rates of the absolute digital divide and the annual rate of catch-up by the low-income group. The faster is the increasing rate of the absolute digital divide, the faster the speed of catch-up by the low-income group of countries becomes. Several policy implications from these findings are discussed.

Purpose: To overcome the shortcomings of traditional industry classification systems such as the Standard Industrial Classification Standard Industrial Classification, North American Industry Classification System North American Industry Classification System, and Global Industry Classification Standard Global Industry Classification Standard, the authors explore industry classifications using machine learning methods as an application of interpretable artificial intelligence (AI).Design/methodology/approach: The authors propose a text-based industry classification combined with a machine learning technique by extracting distinguishable features from business descriptions in financial reports. The proposed method can reduce the dimensions of word vectors to avoid the curse of dimensionality when measuring the similarities of firms.Findings: Using the proposed method, the sample firms form clusters of distinctive industries, thus overcoming the limitations of existing classifications. The method also clarifies industry boundaries based on lower-dimensional information. The graphical closeness between industries can reflect the industry-level relationship as well as the closeness between individual firms.Originality/value: The authors’ work contributes to the industry classification literature by empirically investigating the effectiveness of machine learning methods. The text mining method resolves issues concerning the timeliness of traditional industry classifications by capturing new information in annual reports. In addition, the authors’ approach can solve the computing concerns of high dimensionality.

Do larger cities still suffer from higher crime rates? The scaling relationship between the number of crimes and the population size for the maximum of 758 cities with more than 50,000 inhabitants in the United States from 1999 to 2014 was analyzed. For the total group of cities, the relationship is superlinear for both violent and property crimes. However, for the subgroups of the top 12, top 24, and top 50 largest cities, the relationship changes to sublinear for both violent and property crimes. Results from the panel data analysis are in support of these findings. Along with population size, income per capita and population density also influence the outcome of crime counts. Implications from these findings will be discussed.

Shortly after Groupon started its business in 2008, selling one deal a day with substantial price discounts, daily-deal sites became new online shopping places for many people. Starting with Groupon, most daily-deal sites required that voucher sales be higher than a predetermined number before deals become active. This feature, known as the “tipping point,” was a unique characteristic of the daily-deal business and is identified as one of the most prominent features of social shopping. Most daily-deal sites also required that a redemption period start after a deal was over and be fixed, usually 90 days, presumably to maximize the promotional effect of deals by encouraging rapid voucher redemption. The question remains, however, whether such features actually contributed to the success of the daily-deal industry. Using individual-level panel data from a major daily-deal site in Korea, we analyze whether consumers’ purchase and redemption behaviors were affected by these features and how consumers changed their behaviors as they continued to purchase and redeem vouchers over time. We find that the presence of the tipping point did not boost voucher sales and likely deterred new customers from buying deals right away. We also find that new customers tended to redeem their vouchers quickly, and this likely caused the small businesses that offered deals to become overwhelmed. It is not surprising, given our findings, that both Groupon and the Korean daily deal site abandoned the use of the tipping point and modified redemption rules.

This study focuses on the evolution of platform companies from the perspective of venture ecosystem development. Since traditional tycoons have a governance structure of owner-oriented companies, they can be a platform for production assemblers who have led outsourcing of H/W ventures through closed innovation activities. On the other hand, since IT platform companies have the governance structure of professional management companies, it can be a platform for market makers who mediated the commercialisation of S/W ventures through open innovation activities. Based on these discussions, this study conducted an empirical analysis on Naver, a representative IT platform company in Korea, to investigate the effects of open innovation activities on IT platform companies’ market value. As a result, among the outward-type open innovation activities, ‘Affiliated companies divided into affiliates’ and ‘Affiliated companies started new businesses’ had a positive effect on Naver's market value rather than Naver's headquarter. Second, among inward-type open innovation activities, ‘Headquarter invested in a venture fund’, ‘Affiliated had J/V with external companies’, and ‘Headquarter acquired external companies’ had a positive effect on Naver's market value.