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Smartphone Addiction, Usage Preferences, and Mental Health Among Older Adults in the Digital Age: A Moderated Mediation Model
Get PDFThis study investigates the mechanisms by which smartphone usage preferences affect depressive symptoms among the elderly. Based on a survey of 2,585 individuals, usage is classified into interactive and self-entertainment preferences. The findings reveal that an interactive preference significantly alleviates depressive symptoms by enhancing social participation. In contrast, a self-entertainment preference is positively correlated with higher levels of depression, likely because it can weaken real-world social engagement. Furthermore, the study identifies smartphone addiction as a critical moderating variable. For individuals with a strong interactive preference, addiction negatively moderates the beneficial effect of social participation on depression. The moderating role of addiction on the relationship between self-entertainment preference and social participation also varies depending on the degree of addiction. This research provides nuanced insights into the boundary conditions for promoting the healthy integration of seniors into the digital world
H Tea Company's Path to Listing: A Study on Financing Strategies, Business Models, and Market Performance
Get PDFThis study focuses on H Tea Beverage Industry Co., Ltd., examining its entire IPO process through three core dimensions: financing strategy, business model, and market performance. Findings reveal that H Tea achieved rapid expansion via its asset-light franchise model and completed its listing through differentiated financing approaches. While the IPO optimized its financial structure and brand influence, the company must continue addressing challenges posed by its high price-to-earnings ratio and intense competition within the saturated industry. By integrating exchange announcements, the company's prospectus, and authoritative industry data, this paper provides a case study and practical insights for new consumer enterprises pursuing public listings
Artificial Intelligence Empowering the Internationalization Strategy of SMEs in Emerging Markets: An Empirical Study Based on Dynamic Capabilities and Real Options Theory
Get PDFAmidst the dual backdrop of the digital economy wave and increasing global uncertainty, artificial intelligence (AI) technology is profoundly reshaping the logic and pathways of firm internationalization. This study focuses on small and medium-sized enterprises (SMEs) in emerging markets, aiming to explore how their AI capabilities influence strategic choices in internationalization. By integrating dynamic capabilities theory and real options theory, this paper constructs a theoretical framework, proposing that AI capabilities empower SMEs to adopt more proactive and flexible market entry modes and expansion strategies through two core mechanisms: "environmental uncertainty reduction" and "strategic flexibility generation." Employing a mixed-methods research design that prioritizes questionnaire surveys supplemented by multiple case studies, the study collected and analyzed data from 327 internationalized SMEs in China and Southeast Asia. Structural equation modeling analysis revealed that a firm's AI capabilities (encompassing three dimensions: data analytics, intelligent prediction, and process automation) have a significant positive impact on the degree of internationalization (β = 0.42, p < 0.001) and the preference for low-commitment entry modes (β = 0.38, p < 0.001). Case studies further elucidated the critical role of AI in scenarios such as real-time market insight, supply chain risk simulation, and cross-border intelligent customer service. This research not only expands the theory of firm internationalization in the digital age by conceptualizing AI as a core dynamic capability but also provides practical guidance for SMEs in emerging markets seeking to leverage digital technology for "agile internationalization" within complex global environments
An Empirical Study on the Quality of Chinese-English Translation under Bilingual Prompts Based on Large Language Model
Get PDFThe rapid rise of artificial intelligence in recent years has not only impacted traditional translation project models but also driven innovation in the evaluation methods of translation quality and effectiveness. In large language models, translation prompts have an impact on translation quality. Bilingual prompts also have an impact when facing the same translation task. This paper conducts an empirical study on the research hypothesis using Chinese-English bilingual prompts as the direction, concluding that the language factor of prompts affects translation quality, with impacts at the lexical and syntactic levels
How AI Constructs Stance: A Corpus-Based Comparison of Interactional Metadiscourse in Student and AI Essays
Get PDFThe application of Large Language Models (LLMs) in the writing has prompted a need to scrutinize how those models establish authorship and stance. This study adopts a corpus-based approach to investigate the interactional metadiscourse in AI-generated essays versus human writing, under Hyland’s framework. From a parallel corpus of approximately 1.3 million words from the DAIGT V2 dataset, the research combines quantitative frequency analysis with qualitative concordance investigation. The findings reveal that while AI models demonstrate structural proficiency, they exhibit a deficit in rhetorical conviction and reader engagement characterized by a detached stance. Compared to the dialogic nature of human writing, AI models show underuse of boosters and rhetorical questions, and a reliance on vague attitude markers, and hence lack the capability of building writer-reader relationship. These results highlight the deficiency of AI writing’s stance-taking and offer corresponding pedagogical implications
Exploring Strategy Use in Chinese-English Translation: Insights from Think-Aloud Data
Get PDFThis study investigates the cognitive processes and translation strategies of Chinese student translators by integrating Think-Aloud Protocols (TAP) and Translog data. It aims to uncover how translators with different proficiency levels employ strategies, allocate cognitive effort, and monitor their translation behavior in Chinese-English translation tasks. A comprehensive translation strategy model was proposed to label the data and to interpret the findings. Results indicate that high-performing translators exhibited more balanced cognitive effort, stronger problem diagnosis, and multi-level monitoring, while low-performing translators showed surface-level processing and reactive revisions. The study concludes that translation expertise is characterized by deeper source-text analysis, strategic decision-making, and proactive monitoring. Pedagogically, the findings suggest the need to cultivate students’ metacognitive awareness through think-aloud practice, guided resource use, and structured revision training to enhance translation competence
Applied Research on Improving Oral French Proficiency for French Majors Based on ANL
Get PDFThis study focuses on enhancing oral French proficiency for French majors based on Acceptable Noise Level (ANL) theory. By analyzing the theoretical framework of ANL and its potential applications in language instruction, combined with the characteristics of French oral communication, a teaching model integrating phonetic training, scenario simulation, and interactive feedback is proposed. The study emphasizes constructing a systematic training framework encompassing personalized pronunciation correction, multidimensional scenario-based task design, and dynamic assessment mechanisms. This aims to enhance students' fluency and accuracy in complex linguistic environments. Although experimental validation is not the primary focus, a small-scale case study confirms the theoretical model's feasibility, offering new theoretical perspectives and practical pathways for French oral instruction
Research on the Reform and Innovative Pathways of Translation Teaching in the Era of Artificial Intelligence
Get PDFThe rapid advancement of artificial intelligence, particularly machine translation and large language models, is reshaping translation practice and redefining competency requirements in the translation industry. Traditional teaching models that focus primarily on linguistic knowledge are increasingly insufficient for addressing these changes. While AI technologies have improved translation efficiency and accessibility, they also present new challenges for translator competence and pedagogical design. Situated within the context of the AI era, this study examines the impact of artificial intelligence on translation teaching and identifies key problems in current university-level education, including misaligned teaching objectives, limited instructional content, and inadequate evaluation systems. In response, the paper proposes reform principles centered on competency development, human–machine collaboration, and the integration of humanistic values. It further explores practical approaches such as curriculum restructuring, teaching model innovation, and diversified assessment methods. The study concludes that the effective integration of artificial intelligence can promote a shift from single-skill language training to comprehensive competence development, thereby enhancing teaching quality and the practical relevance of translation education
Functional Analysis of the “the X is” Construction: A Grammaticalization Perspective
Get PDFThe different propositional meanings and degrees of grammaticalization of head nouns (X) in the source structure are the root cause of the marked differences in the synchronic usage of the “the X is”-type expressions. The grammaticalization gradience of typical expressions conforming to this structure may be presented from high to low as follows: the thing is > the truth is/the fact is > the trouble is/the point is/the problem is> the reason is/.... Among them, the thing is is grammaticalized to a relatively high degree, and thus is more frequently used as a discourse marker; the trouble is, the point is and the problem is are grammaticalized to a relatively lower degree, and therefore are primarily used as comment clauses; most expressions of this type as in the case of the reason is, are minimally grammaticalized and retain their propositional meaning as matrix clauses
How Epistemological Disjunctivism Aligns with Fine-Grained Belief
Get PDFEpistemological Disjunctivism (ED), as a well-known anti-skeptical strategy, claims that in good perceptual cases, the subject’s perceptual experience itself constitutes factive reasons that are sufficient to directly support the knower’s knowledge. However, critics such as Ranalli (2014) have pointed out that ED’s account of the role of “belief” in knowledge ascription is in fact unclear. This calls for an explanation of how perceptual experience can attain reflective accessibility in good cases; otherwise, the thesis that “experience = reason” lacks persuasiveness. To respond to this challenge, this paper proposes a “layered belief framework” to supplement the shortcomings of the original ED. I will argue that this proposal not only effectively responds to the criticisms from Ranalli and others, but also provides ED with new explanatory dimensions that bring it closer to human cognitive psychology. At the same time, I will also demonstrate that its anti-skeptical capacity can only be regarded as a local defense strategy