Information Technology and Management最新文献

Large language models (LLMs) such as ChatGPT play a crucial role in guiding critical decisions nowadays, such as in choosing a college major. Therefore, it is essential to assess the limitations of these models’ recommendations and understand any potential biases that may mislead human decisions. In this study, I investigate bias in terms of GPT-3.5 Turbo’s college major recommendations for students with various profiles, looking at demographic disparities in factors such as race, gender, and socioeconomic status, as well as educational disparities such as score percentiles. To conduct this analysis, I sourced public data for California seniors who have taken standardized tests like the California Standard Test (CAST) in 2023. By constructing prompts for the ChatGPT API, allowing the model to recommend majors based on high school student profiles, I evaluate bias using various metrics, including the Jaccard Coefficient, Wasserstein Metric, and STEM Disparity Score. The results of this study reveal a significant disparity in the set of recommended college majors, irrespective of the bias metric applied. Notably, the most pronounced disparities are observed for students who fall into minority categories, such as LGBTQ + , Hispanic, or the socioeconomically disadvantaged. Within these groups, ChatGPT demonstrates a lower likelihood of recommending STEM majors compared to a baseline scenario where these criteria are unspecified. For example, when employing the STEM Disparity Score metric, an LGBTQ + student scoring at the 50th percentile faces a 50% reduced chance of receiving a STEM major recommendation in comparison to a male student, with all other factors held constant. Additionally, an average Asian student is three times more likely to receive a STEM major recommendation than an African-American student. Meanwhile, students facing socioeconomic disadvantages have a 30% lower chance of being recommended a STEM major compared to their more privileged counterparts. These findings highlight the pressing need to acknowledge and rectify biases within language models, especially when they play a critical role in shaping personalized decisions. Addressing these disparities is essential to foster a more equitable educational and career environment for all students.

While smart cities have been initiated by various city governments around the world in recent years, digital transformations with regard to services have become essential for a city to be smart. Nonetheless, few previous studies have explored the adoption intentions toward smart city services, especially from a perspective of social learning. This study aims to investigate the factors that influence the adoption intentions toward smart city services among users. Based on social learning theory, this study develops a research model that integrates adoption intentions and five factors: perceived usefulness, perceived ease of use, trust, social influence, and government support. This study examined this model via survey data from 940 respondents in Taiwan. The results reveal that perceived usefulness, perceived ease of use, and trust have positive effects on adoption intentions and that social influence and government support have impacts on adoption intentions through trust. The results offer a useful reference for other countries in the early stages of smart city initiatives, and they have significant theoretical implications for researchers in the areas of smart cities and innovative service adoption.

Online health communities can help patients with chronic diseases to better self-manage their health and provide an effective channel for doctor-patient and patient-patient health value co-creation. However, fewer studies have explored the factors influencing chronic disease patients’ participation in health value co-creation in online health communities from a comprehensive perspective. By combining a mixed method of qualitative and quantitative research, this study established a research model based on the socio-technical systems theory to systematically explore the factors influencing the participation of patients with chronic diseases in health value co-creation. Data were collected from patients with chronic diseases who had used an online health community and partial least square-structural equation modelling was used to test the data. The results revealed that the impact of interactivity on information and emotional support, as well as the impact of social support and service convenience on health value co-creation intention, were significant. The moderating effect of service convenience on the impact of emotional support on health value co-creation has also been validated. Originality: This study provides insights to further understand the psychological characteristics of patients with chronic diseases and optimize the use of online health platforms. From a health management perspective, this study promotes an understanding of the online behavioral characteristics of patients with chronic diseases.

Artificial intelligence (AI) has become the core driving force for innovation and development of manufacturing enterprises. This paper selects Haier COSMOPLAT as a case study to systematically discuss the evolution process and internal mechanism of AI-enabled manufacturing enterprise innovation. First, in the start-up stage, the industrial internet platform empowers manufacturing innovation along the path of resource patchwork to platform empowerment to dependency-oriented symbiosis, promoting the cocreation of economic value between manufacturing enterprises and platforms. Next, in the growth stage, the industrial internet platform empowers manufacturing enterprise innovation along the path of resource orchestration to field empowerment to nested symbiosis, boosting the cocreation of network value between manufacturing enterprises and platforms. Finally, in the maturity stage, the industrial internet platform empowers manufacturing enterprises to innovate along the path of resource coordination to ecological empowerment to equality symbiosis, advancing the cocreation of ecological value between manufacturing enterprises and platforms. This study not only enriches AI-enabled manufacturing innovation research area but also provides beneficial management enlightenment to accelerate the intelligent transformation and upgrading of the manufacturing industry.

Leveraging two prominent theories of technology adoption in firms, this study examines the organizational determinants of the adoption intensity of artificial intelligence (AI) and its effects on firms’ performance, under the moderating effects of technological turbulence. To conduct this study, a unique dataset was compiled via a survey of US-based managers involved with technology and AI adoption in high-tech goods and services, leading to 226 usable responses. Structural Equation Modeling was then applied to test the proposed model. The findings uncover the influence of technological, organizational, and environmental factors on the firms’ AI adoption intensity. Additionally, a positive correlation is observed between AI adoption intensity and firms' performance. Lastly, technological turbulence emerges as a crucial environmental factor moderating the effects of antecedents on AI. Given the feeble adoption of AI in firms despite its documented role in firms’ success, the current study can offer a road map to successfully implementing AI in firms and, thus, improving their performance.