In the rapidly evolving technological landscape, state-owned enterprises (SOEs) encounter significant challenges in sustaining their competitiveness through efficient R&D management. Integrated Product Development (IPD), with its emphasis on cross-functional teamwork, concurrent engineering, and data-driven decision-making, has been widely recognized for enhancing R&D efficiency and product quality. However, the unique characteristics of SOEs pose challenges to the effective implementation of IPD. The advancement of big data and artificial intelligence technologies offers new opportunities for optimizing IPD R&D management through data-driven decision-making models. This paper constructs and validates a data-driven decision-making model tailored to the IPD R&D management of SOEs. By integrating data mining, machine learning, and other advanced analytical techniques, the model serves as a scientific and efficient decision-making tool. It aids SOEs in optimizing R&D resource allocation, shortening product development cycles, reducing R&D costs, and improving product quality and innovation. Moreover, this study contributes to a deeper theoretical understanding of the value of data-driven decision-making in the context of IPD.
Social media has emerged as a major global influence on consumer behavior in recent years. User-generated, interactive content is made possible by platforms like Facebook, Instagram, YouTube, and TikTok, and it has a big impact on opinions and buying decisions. Therefore, the objective of this research was to examine the influence of social media (online reviews, social media influencers, eWOM, advertisements, social media usage laws, and information) on consumers’ purchase decisions of electronic products. Guided by the theory of planned behavior and social influence theory, a quantitative approach was used, with data collected via a structured questionnaire from 150 respondents over four weeks. Analysis was conducted using SPSS 30. SPSS analysis demonstrated that social media usage laws and social media influencers had a strong positive correlation with consumers’ purchase decisions, highlighting their pivotal role in shaping preferences and disseminating information. Reliable and accessible product information also emerged as a significant predictor of consumer choices. In contrast, online reviews, eWOM, and advertisements exhibited minimal influence within this context or might have adverse influence. This article provides guidelines for electronic products to prioritize social media strategies, invest in high-quality information dissemination, and leverage influencer partnerships to effectively engage consumers and drive sales.
Investors are always willing to receive more data. This has become especially true for the application of modern portfolio theory to the institutional asset allocation process, which requires quantitative estimates of risk and return. When long-term data series are unavailable for analysis, it has become common practice to use recent data only. The danger is that these data may not be representative of future performance. Although longer data series are of poorer quality, are difficult to obtain, and may reflect various political and economic regimes, they often paint a very different picture of emerging market performance. This paper presents an application of a stochastic nonlinear optimization model of portfolios including transaction costs in the Brazilian financial market. In order to have that, portfolio theory and optimal control were used as theoretical basis. The first strategy tries to allocate the whole available wealth, not considering the risk associated to portfolio (deterministic result). In this case the investor obtained profits of 7,23% a month, taking into account the three risk aversion levels during the whole planning period [see column (7)]. On the contrary, the results from of the stochastic algorithm obtained profits of 1,34% a month and 18,06% a year, if the investor has low risk aversion. The profits would be 0,88% a month and 11,02% a year for a medium risk aversion investor. And with high risk aversion, the investor obtains 0,62% a month and 7,66% a year.
Within the framework of the 2030 Agenda and to achieve the Sustainable Development Goals (SDGs), science, technology and innovation play an even more central role. Building on this foundation, the primary objective of this paper is to explore the potential applications of blockchain in supporting the achievement of these sustainability goals. Starting from a review of the relevant literature on this topic, the main fields in which blockchain can contribute to sustainable development will be identified. The main blockchain applications will then be analyzed and categorized according to these SDGs. This research will then critically present the main blockchain-based projects that emerged in the first stage of the study and were implemented by the United Nations. The main objectives and benefits of each project will be analyzed. This is where the originality of this paper lies. To the best of the author’s knowledge, this is one of the first attempts to present a comprehensive overview of the United Nations’ projects related to SDGs 1, 2, 5, 7, 9, 13, and 16. This paper, which bridges the gap between innovation management and the sustainability field, will contribute to the increasingly current debate on sustainability issues and be beneficial to scholars, practitioners, and policymakers alike.
