The use of artificial intelligence (AI) is related to the dynamic development of digital skills. This article focuses on the impact of AI on the work of non-profit organizations that aim to help those around them. Based on 10 semi-structured interviews, it is presented here how it is possible to work with AI and in which areas it can be used—in social marketing, project management, routine bureaucracy. At the same time, workers and volunteers need to be educated in critical and logical thinking more than ever before. These days, AI is becoming more and more present in almost all the activities, bringing several benefits to those making use of it. On the one hand, by using AI in the day-to-day activities, the entities are able to substantially decrease their costs and have the advantage of being able to have, in most cases, a better and faster job done. On the other hand, those individuals that are more creative and more innovative in their line of work should not feel threatened by those situations in which organizations decide to use more AI technologies rather than human beings for the routine activities, since they will get the opportunity to perform tasks that truly require their intellectual capital and decision making abilities.

The proportion of national logistics costs to Gross Domestic Product (NLC/GDP) serve as a valuable indicator for estimating a country’s overall macro-level logistics costs. In some developing nations, policies aimed at reducing the NLC/GDP ratio have been elevated to the national agenda. Nevertheless, there is a paucity of research examining the variables that can determine this ratio. The purpose of this paper is to offer a scientific approach for investigating the primary determinants of the NLC/GDP and to advice policy for the reduction of macro-level logistics costs. This paper presents a systematic framework for identifying the essential criteria for lowering the NLC/GDP score and employs co-integration analysis and error correction models to evaluate the impact of industrial structure, logistics commodity value, and logistics supply scale on NLC/GDP using time series data from 1991 to 2022 in China. The findings suggest that the industrial structure is the primary factor influencing logistics demand and a significant determinant of the value of NLC/GDP. Whether assessing long-term or short-term effects, the industrial structure has a substantial impact on NLC/GDP compared to logistics supply scale and logistics commodity value. The research offers two policy implications: firstly, the goals of reducing NLC/GDP and boosting the logistics industry’s GDP are inherently incompatible; it is not feasible to simultaneously enhance the logistics industry’s GDP and decrease the macro logistics cost. Secondly, if China aims to lower its macro-level logistics costs, it must make corresponding adjustments to its industrial structure.

Introduction: Chatbots are increasingly utilized in education, offering real-time, personalized communication. While research has explored technical aspects of chatbots, user experience remains under-investigated. This study examines a model for evaluating user experience and satisfaction with chatbots in higher education. Methodology: A four-factor model (information quality, system quality, chatbot experience, user satisfaction) was proposed based on prior research. An alternative two-factor model emerged through exploratory factor analysis, focusing on “Chatbot Response Quality” and “User Experience and Satisfaction with the Chatbot.” Surveys were distributed to students and faculty at a university in Ecuador to collect data. Confirmatory factor analysis validated both models. Results: The two-factor model explained a significantly greater proportion of the data’s variance (55.2%) compared to the four-factor model (46.4%). Conclusion: This study suggests that a simpler model focusing on chatbot response quality and user experience is more effective for evaluating chatbots in education. Future research can explore methods to optimize these factors and improve the learning experience for students.

This study will explore the direct and indirect impacts of collaborative governance innovation on organizational value creation in higher vocational education in China in the context of the digital era. This paper employs a mixed research methodology to construct and validate a model of the relationship between collaborative governance, digital competence, value chain restructuring, and value creation. This study first adopted an exploratory sequential design. In the qualitative interviews, 15 experts from education, business, and other related fields were used as respondents to explore accurate variable factors and determine the value of the research framework. The quantitative research used structural equation analysis to analyze 979 valid online questionnaires. Finally, the rationality of the research results was verified through case studies.  The findings are clear: collaborative governance significantly positively impacts value creation, indirectly affecting organizational value creation through value chain restructuring. Furthermore, digital capabilities significantly contribute to the value chain restructuring process. This paper provides a theoretical basis and practical guidance for higher vocational education organizations to improve their governance and innovation capabilities.

Real estate appraisal standards provide guidelines for the preparation of reliable valuations. These standards emphasize the central role of market data collection in market-oriented valuation methodologies such as the Market Comparison Approach (MCA), which is the most commonly used. The objective of this study is to highlight the difficulties in data finding, as well as the gap between the standards and the actual appraisal practices in Italy. Thus, a detailed comparison was made between the real estate data considered necessary by the standards and those ones reasonably detectable by appraisers, showing that some important market information is not reachable due to legal, technical and economic factors. Finally, a case study is presented in which the actual appraisal of a residential property is schematically described to support what is claimed with the research question and thus the degree of uncertainty around an estimate judgment.

This research explores the critical influence of corporate culture on small and medium-sized enterprises’ (SMEs) crisis response abilities under varied cross-cultural environments. Amid the disruptive backdrop of the COVID-19 pandemic, SMEs globally have faced unprecedented challenges. This study addresses a gap in the existing literature by conducting a cross-cultural analysis of SMEs in China, Thailand, and Germany to understand how corporate culture affects crisis management. Utilizing a competitive cultural value model, the research categorizes corporate culture into four dimensions: group culture, development culture, hierarchy culture, and rational culture. These cultural dimensions are investigated in relation to their impact on crisis response abilities. Additionally, national cultural dimensions such as individualism and uncertainty avoidance are examined as moderating variables. The findings reveal that group and development cultures positively influence crisis response abilities, enhancing organizational resilience and adaptability. Conversely, hierarchy culture negatively affects crisis management, hindering flexible response strategies. Rational culture supports structured crisis response through goal-oriented practices. National culture significantly moderates these relationships, with individualism and high uncertainty avoidance impacting the effectiveness of organizational cultural dimensions in crisis scenarios. This study offers theoretical advancements by integrating cultural dimensions with crisis response strategies and provides practical implications for SMEs striving to enhance their resilience and adaptability in a globalized business environment.