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This study aims to assess the effectiveness of different AI models in accurately aggregating information about the protein quality (PQ) content of food items using four artificial intelligence (AI) models -– ChatGPT 3.5, ChatGPT 4, Bard AI and Bing Chat.

A total of 22 food items, curated from the Food and Agriculture Organisation (FAO) of the United Nations (UN) report, were input into each model. These items were characterised by their PQ content according to the Digestible Indispensable Amino Acid Score (DIAAS).

Bing Chat was the most accurate AI assistant with a mean accuracy rate of 63.6% for all analyses, followed by ChatGPT 4 with 60.6%. ChatGPT 4 (Cohen’s kappa: 0.718, p < 0.001) and ChatGPT 3.5 (Cohen’s kappa: 0.636, p: 0.002) showed substantial agreement between baseline and 2nd analysis, whereas they showed a moderate agreement between baseline and 3rd analysis (Cohen’s kappa: 0.538, p: 0.011 for ChatGPT 4 and Cohen’s kappa: 0.455, p: 0.030 for ChatGPT 3.5).

This study provides an initial insight into how emerging AI models assess and classify nutrient content pertinent to nutritional knowledge. Further research into the real-world implementation of AI for nutritional advice is essential as the technology develops.

The authors study how cloud adoption decision making unfolds in organizations and present the dynamic process leading to a decision to adopt or reject cloud computing. The authors thus complement earlier literature on factors that influence cloud adoption.

The authors adopt an interpretive epistemology to understand the process of cloud adoption decision making. Following an empirical investigation drawing on interviews with senior managers who led the cloud adoption decision making in organizations from across Europe. The authors outline a framework that shows how cloud adoptions follow multiple cycles in three broad phases.

The study findings demonstrate that cloud adoption decision making is a recursive process of learning about cloud through three broad phases: building perception about cloud possibilities, contextualizing cloud possibilities in terms of current computing resources and exposing the cloud proposition to others involved in making the decision. Building on these findings, the authors construct a framework of this process which can inform practitioners in making decisions on cloud adoption.

This work contributes to authors understanding of how cloud adoption decisions unfold and provides a framework for cloud adoption decisions that has theoretical and practical value. The study further demonstrates the role of the decision-leader, typically the CIO, in this process and identifies how other internal and external stakeholders are involved. It sheds light on the relevance of the phases of the cloud adoption decision-making process to different cloud adoption factors identified in the extant literature.

Coordinating low-carbonization and digitalization is a practical implementation pathway to achieve high-quality economic development. Regions are under great emission reduction pressure to achieve low-carbon development. However, why and how regional emission reduction pressure influences enterprise digital transformation is lacking in the literature. This study empirically tests the impact of emission reduction pressure on enterprise digital transformation and its mechanism.

This article takes the data of non-financial listed companies from 2011 to 2020 as a sample. The digital transformation index is measured by entropy value method. The bidirectional fixed effect model was used to test the hypothesis.

The research results show that emission reduction pressure forces enterprise digital transformation. The mechanism lies in that emission reduction pressure improves digital transformation by promoting enterprise innovation, and digital economy moderates the nexus between emission reduction pressure and digital transformation. Furthermore, the effect of emission reduction pressure on digital transformation is more significant for non-state-owned, mature and high-tech enterprises.

This paper discusses the mediating role of enterprise innovation between carbon emission reduction pressure and enterprise digital transformation, as well as the moderating role of digital economy. The research expands the body of knowledge about dual carbon targets, digitization and technological innovation. The author’s findings help update the impact of regional digital economy development on enterprise digital transformation. It also provides theoretical guidance for the realization of digital transformation by enterprise innovation.