論文

Distributed data use that links datasets across fields and organizations has drawn attention as a source of innovation. However, evidence remains limited regarding which factors promote such data use on co-creation platforms. Using large-scale archival data from Kaggle, one of the world's largest data analysis platforms, this study employs multiple regression analysis to examine how data characteristics and data owner characteristics affect the degree of data use. The results show that peer evaluations (upvotes) and explicit owner rankings are positively associated with both data viewing and downstream use. These findings offer practical implications for the governance of data platforms and for designing policies that effectively foster data use.

The attention-based view posits that a firm's allocation of attention to particular issues directly influences its actions and performance. Yet, the impact of attentional uniqueness – how the pattern of a firm's attentional allocation diverges from its competitors within the same industry – on behaviour and performance remains underexplored. We argue for an inverted U-shaped relationship between attentional uniqueness and firm performance, mediated by the frequency of growth actions. This is because a firm's attentional allocation shapes its reaction to problems, opportunities, and threats in the competitive landscape, resulting in its competitive advantage. To generate growth actions, a firm needs to have both a unique perspective and a general understanding of its industry. Furthermore, we propose that this relationship is contingent on environmental munificence, which reflects the presence of growth opportunities. Our analysis, leveraging structural topic modelling on annual security reports from 986 Japanese listed companies between 2004 and 2016, broadly supports these theoretical predictions.

Purpose – The rise of online hate speech around the world in recent years has prompted many nations to reassess their laws and consider harsher penalties for offenders to deter such harmful behavior and foster a more positive online environment. This study aims to investigate the effectiveness of such legislation in Japan, where a revised law was enacted in June 2022, significantly increasing fines and introducing potential jail time for online insults. Design/methodology/approach – Our study employs a mixed-methods approach, combining an observational analysis of Japanese Twitter data with a survey of Japanese undergraduate students. Findings – We find that this stricter law has had a minimal impact on the behavior of Twitter users. Specifically, the toxicity level of Japanese tweets has not shown substantial changes since the law’s implementation. Meanwhile, our survey finds that the majority of survey participants are not aware of the legislation change. Additionally, survey participants significantly underestimate the penalties for offenders and are not particularly confident that offenders will be arrested. These findings underscore the critical role of public awareness in effectively deterring undesirable online conduct through legislation. Originality/value – Our work is among the first empirical studies to evaluate the real-world impact of online hate speech legislation in Japan. It emphasizes that stricter law alone is insufficient to influence behavior without public awareness and trust in enforcement.

Purpose – This study aims to evaluate the impact of Japan’s 2022 online hate speech law on toxic content on digital platforms. Specifically, this work investigates whether the legislation led to a measurable change in the prevalence and intensity of toxic online content on Twitter and 5ch. This research seeks to contribute to broader discussions on digital governance, policy deterrence and content moderation in the context of rising global concern over online hate speech. Design/methodology/approach – A natural experiment methodology was used, leveraging a large-scale dataset of over 16 million tweets and 100, 000 posts from the anonymous forum 5ch. The analysis used Google’s Perspective API to quantify the toxicity of user-generated content before and after the enactment of the law. Comparative analysis was conducted across platforms and user types, focusing on changes in toxic volume and intensity. Findings – Contrary to the legislation’s goals, there was no statistically significant reduction in toxic content. On Twitter, both the frequency and severity of toxicity increased post-law, especially among repeat offenders. Meanwhile, 5ch displayed no notable change in toxic behavior. These findings suggest that the law does not deter the production of toxic content and may have had unintended consequences on certain user behaviors. Originality/value – This study offers one of the first empirical assessments of Japan’s 2022 hate speech law using behavioral data from major platforms. It challenges classical deterrence theory and provides novel insights into the limitations of blanket legal measures. This research underscores the need for context-aware, adaptive policy frameworks that reflect platform-specific dynamics and user behaviors in digital environments.

Value creation by reusing data as exchangeable resources has been widely studied as a new source of innovation, resulting in the establishment of a data market and its ecosystem. However, owing to the nascent nature of the market, observable information on exchanged datasets and interactions among stakeholders, such as buyers and providers, in the market are limited. Therefore, a lack of observable information that contributes to designing the market system and formulating regulations to promote the sound growth of data markets exists. This study modeled exchanged datasets and data buyers as agents, the smallest units of the data market components, and prepared seven scenarios for four market sizes. We simulated the effects of datasets and agent models with different market sizes on the agents' data purchases and emergence of popular datasets and discussed the factors that affect the purchase frequency distribution of the datasets. We present the experimental results and new implications for developing a data market simulator. The development of this simulator is expected to significantly advance research on the market understanding and system design in data markets.

In recent years, there have been growing expectations for the creation of new businesses and the improvement of the value of existing services by exchanging data in different fields. Data stored in-house within organizations have become a new source of innovation. While there is a high need for the value creation of data, determining the data value is not an easy task, as there is a wide range of factors to be considered, such as data pricing, acquisition cost, usage value, and update frequency. In this study, we observe communication, such as the sharing of know-hows in data exchange and analysis, and discuss the growing process of a community on the data platform. For the experiment, we focused on the data community in the COVID-19 disaster and used a unique dataset from the data platform Kaggle, which is the data analysis competition service. The results suggest that user actions differ in the discussion of the dataset and analysis. Moreover, providing topics, user participation, and activating actions in the early stages after the dataset is released are essential for forming a data community. We argue that the actions on the data analysis, such as comments and votes, are also crucial for fostering a common understanding of the data value.

The purpose of this paper is to examine how an actor strategically develops and diffuses technology standards that align with innovation trajectories while maintaining a consensus with competitors. We conduct a field study of HTML5 standardization and examine how Google strategically influences the development and diffusion of HTML5 toward their favorable standard trajectories. We show that Google has adopted two strategic policies (integrating outside technologies and avoiding the monetization of technologies) and engaged in two relational practices (forming alliances with browser vendors and engaging developer communities) to realize an open Web application platform on the HTML5 while competing and coordinating with other actors. Google attracts application developers and browser vendors to collaboratively develop HTML5 specifications and HTML5-compatible products and services, which have enriched Google's open Web application strategy. These relational practices were enabled and amplified by non-commercial policies for corresponding web applications and the use of other parties' technologies.

様々な産業・経済システムのデジタル化が進展する中、デジタルデータを活用した価値創造への期待が高まっている。その流れを受けて、異なる事業者間でデータを交換・取引するデータ流通エコシステムが萌芽し、新たなイノベーションの源泉として注目され始めている。このようにデータ流通を通じた価値創造への関心は高まっているものの、法制度設計と技術仕様が複雑に絡み合うことから、データ流通エコシステム全体を包括的に捉えた議論が進んでいるとは言い難い。加えて、データ流通プラットフォームの仕組みやサービスについてのシステム面・ビジネス面に絞られた形での議論は存在する一方で、実際に取り扱われるデータ自体の特徴やデータ間の関係性などのデータ流通メカニズムについて十分には明らかになっていない。そこで本論文では、データ流通プラットフォームにおけるデータ連携・相互作用メカニズムを、実証分析に基づいて検討した。プラットフォーム上のデータネットワーク分析から、1) 連携可能性を高めるデータの特徴、2) 連携を促進するデータ共有条件、及び3) データネットワーク構造の特徴に関する知見を得た。具体的には、「時間」や「場所」などに関わる変数が多様な異種データの連携可能性を高めることが示された。また、秘匿データと共有可能データの混在が密なネットワーク構造の構成を促し、異分野間データ連携の成立可能性を高めることが明らかになった。加えて、データネットワークは人間関係と似た「局所的に密、大域的に疎」な構造をしており、疎の部分を埋めるデータを提供することで、異分野をつなげるデータ活用を活性化できる可能性が示唆された。これら分析結果をもとに、データ流通プラットフォームの設計・運営及びデータエコノミー推進への示唆と課題を議論する。

AbstractIn recent years, rather than enclosing data within a single organization, exchanging and combining data from different domains has become an emerging practice. Many studies have discussed the economic and utility value of data and data exchange, but the characteristics of data that contribute to problem-solving through data combination have not been fully understood. In big data and interdisciplinary data combinations, large-scale data with many variables are expected to be used, and value is expected to be created by combining data as much as possible. In this study, we conducted three experiments to investigate the characteristics of data, focusing on the relationships between data combinations and variables in each dataset, using empirical data shared by the local government. The results indicate that even datasets that have a few variables are frequently used to propose solutions for problem-solving. Moreover, we found that even if the datasets in the solution do not have common variables, there are some well-established solutions to these problems. The findings of this study shed light on the mechanisms behind data combination for solving problems involving multiple datasets and variables.