Seongmin Jeon

Generative artificial intelligence (GenAI) is rapidly diffusing across firms, but less is known about whether early adoption produces meaningful innovation outcomes among small and medium-sized enterprises (SMEs). This study examines the determinants of GenAI adoption and distinguishes between two downstream applications: product innovation and process innovation. Drawing on survey data from 121 SMEs, we analyze how perceived opportunity, perceived threat, technological importance, internal technical capability, and firm characteristics relate to GenAI adoption and innovation outcomes. Logistic regression results show that perceived opportunity and technological importance significantly predict GenAI adoption, whereas perceived threat and internal technical capability do not. OLS results show that perceived opportunity and GenAI adoption are positively associated with product innovation, measured as GenAI use for strengthening existing and new products/services. Mediation analysis indicates that GenAI adoption partially mediates the relationship between perceived opportunity and product innovation. In contrast, process innovation, measured as GenAI use for improving internal/operational performance, is weakly explained by the same predictors. A paired comparison further shows that SMEs are significantly more likely to report GenAI use for product innovation than for process innovation. These findings reveal an adoption-integration gap: SMEs appear willing to adopt GenAI and apply it to product-facing innovation, but have not yet systematically embedded GenAI into internal operational processes.

Recipient financial need is a crucial factor in donation decisions. This study proposes a novel model for determining financial donations, incorporating consumption levels of both donor and recipient within a societal context. Solving our model’s utility maximization problem reveals how consumption, donation, and savings are interlinked. Empirical evidence reinforces these findings, aligning with prior research and showing that larger consumption gaps between donors and recipients lead to increased donations. Our findings point towards an inherent altruistic motivation in donation, where elevating the recipient’s well-being ultimately enhances the donor’s own utility. This reinforces the notion that consideration of the recipient’s financial hardship, as reflected by their consumption patterns, is crucial when making donation decisions. Shifting beyond traditional models, this study introduces a groundbreaking approach to financial donations. Our novel model factors in consumption levels of both the donor and recipient, along with the broader societal context, using utility maximization to unravel the intertwined decisions of consumption, donation, and savings. Real-world data validates our model, confirming known donation factors and revealing a key finding: larger disparities in consumption lead to increased giving, suggesting an altruistic drive where helping others boosts personal satisfaction.

The paper seems to introduce an economic and social perspective of the Artificial Intelligence. It deals with the issues of investment, education and research. I would point out that the scope of the research is beyond a single paper. Also, I do not...

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.

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.

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.

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.