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The purpose of this paper is to answer two research questions which are “What are key factors which influence Chinese to adopt mobile technology?” and “Do these key factors differ from factors which are identified from western context?”.

The findings from a pilot study with 45 in-depth interviews are used to develop questionnaires and test across 800 residents from the three research cities. The data were analyzed by structural equation modeling together with multi-group analysis.

The data suggest eight important concepts, i.e. utilitarian expectation, hedonic expectation, status gains, status loss avoidance, normative influence, external influence, cost, and quality concern, are influential factors affecting users’ intentions to adopt 3G mobile technology. Differences are found between the samples in the three research cities in the effect of hedonic expectation, status gains, status loss avoidance, and normative influence on mobile technology adoption intention.

As the stability of intentions may change over time, only measuring intentions might be inadequate in predicting actual adoption behaviors. However, the focus on potential users is thought to be appropriate, given that the development of 3G is still in its infancy in China.

Previous research into information technology adoption among Chinese users has not paid attention to regional diversity. Some research considered China as a large single market and some was conducted in only one province or one city. Culturally, China is a heterogeneous country.

This paper aims to propose a method for better discovering topic evolution path and semantic relationship from the perspective of patent entity extraction and semantic representation. On the one hand, this paper identifies entities that have the same semantics but different expressions for accurate topic evolution path discovery. On the other hand, this paper reveals semantic relationships of topic evolution for better understanding what leads to topic evolution.

Firstly, a Bi-LSTM-CRF (bidirectional long short-term memory with conditional random field) model is designed for patent entity extraction and a representation learning method is constructed for patent entity representation. Secondly, a method based on knowledge outflow and inflow is proposed for discovering topic evolution path, by identifying and computing semantic common entities among topics. Finally, multiple semantic relationships among patent entities are pre-designed according to a specific domain, and then the semantic relationship among topics is identified through the proportion of different types of semantic relationships belonging to each topic.

In the field of UAV (unmanned aerial vehicle), this method identifies semantic common entities which have the same semantics but different expressions. In addition, this method better discovers topic evolution paths by comparison with a traditional method. Finally, this method identifies different semantic relationships among topics, which gives a detailed description for understanding and interpretation of topic evolution. These results prove that the proposed method is effective and useful. Simultaneously, this method is a preliminary study and still needs to be further investigated on other datasets using multiple emerging deep learning methods.

This work provides a new perspective for topic evolution analysis by considering semantic representation of patent entities. The authors design a method for discovering topic evolution paths by considering knowledge flow computed by semantic common entities, which can be easily extended to other patent mining-related tasks. This work is the first attempt to reveal semantic relationships among topics for a precise and detailed description of topic evolution.

The purpose of this paper is to analyze the influence that the financial restrictions of Chinese enterprises exert on their green innovation abilities with their increased integration into the global supply chain (GSC).

This study uses customs, import, and export data for 222,773 Chinese enterprises and examined them by ownership type, capital density, and degree of pollution.

The results show that the deeper the integration into the GSC, the looser the financing environment would be, and the stronger the green innovation abilities of the enterprises.

The findings suggest that China should step up privatization of state-owned enterprises, increase government subsidies to private enterprises, and loosen their financing restrictions to address the recent economic decline in the country and ensure smooth and fast economic growth.

This paper is one of the first of its kind to develop and empirically analyze the relationship between the GSC and the financing restrictions and their determinant factors in China. It uniquely contributes to help the authors find approaches to constructing China’s green innovation and has far-reaching implications for other developing countries.

Artificial intelligence (AI) is one of the latest digital transformation (DT) technological trends the university library can use to provide library users with alternative educational services. AI can foster intelligent decisions for retrieving and sharing information for learning and research. However, extant literature confirms a low adoption rate by the university libraries in using AI to provide innovative alternative services, as this is missing in their strategic plan. The research develops (AI-LSICF) an artificial intelligence library services innovative conceptual framework to provide new insight into how AI technology can be used to deliver value-added innovative library services to achieve digital transformation. It will also encourage library and information professionals to adopt AI to complement effective service delivery.

This study adopts a qualitative content analysis to investigate extant literature on how AI adoption fosters innovative services in various organisations. The study also used content analysis to generate possible solutions to aid AI service innovation and delivery in university libraries.

This study uses its findings to develop an Artificial Intelligence Library Services Innovative Conceptual Framework (AI-LSICF) by integrating AI applications and functions into the digital transformation framework elements and discussed using a service innovation framework.

In research, AI-LSICF helps increase an understanding of AI by presenting new insights into how the university library can leverage technology to actualise innovation in service provision to foster DT. This trail will be valuable to scholars and academics interested in addressing the application pathways of AI library service innovation, which is still under-explored in digital transformation.

In practice, AI-LSICF could reform the information industry from its traditional brands into a more applied and resolutely customer-driven organisation. This reformation will awaken awareness of how librarians and information professionals can leverage technology to catch up with digital transformation in this age of the fourth industrial revolution.

The enlightenment of AI-LSICF will motivate library professionals to take advantage of AI's potential to enhance their current business model and achieve a unique competitive advantage within their community.

AI-LSICF development serves as a revelation, motivating university libraries and information professionals to consider AI in their strategic plan to enable technology to support university education. This act will enable alternative service delivery in the face of unforeseen circumstances like technological disruption and the present global COVID-19 pandemic that requires non-physical interaction.