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To build a more comprehensive understanding of factors affecting the success of service contracting, the purpose of this paper is to investigate the influences of service complexity, contract structure and contracting process on the buyer-perceived supplier performance in business-to-business (B2B) services.

A research model is developed based on transaction cost economics and the research on service contracting. The model is tested by the survey data collected. Professional focus groups on LinkedIn are used to generate the list of potential respondents. The sample consists of 177 purchasing professionals from 25 countries.

The results indicate that three major contract dimensions and follow-up management practices positively influence buyer-perceived supplier performance. Furthermore, service complexity amplifies the effects of incentives designed in the contract and the buyer’s follow-up contract management on perceived supplier performance.

The sample consists of respondents from 25 countries and provides good geographic coverage. However, the results should be generalized with caution because not all countries were represented equally.

The study suggests a framework and guidelines for purchasing managers to improve the design and management of service contracts to secure good performance from their supplier.

This paper contributes to understanding the performance-enhancing aspects of designing and monitoring service contracts in B2B contexts. It also adds to the knowledge of the role of service complexity in successful B2B service purchasing.

The return behavior of customers has a great impact on the e-retail industry and has resulted in the emergence of return-freight insurance (RI). Additionally, customer loss aversion arising from returns affects e-retailers' decisions and manufacturers' profits. Therefore, the main purpose of the authors' study is to determine how e-retailers and manufacturers choose their RI strategy and pricing according to customers' loss aversion.

The authors propose three scenarios: no RI, customer purchase RI and free e-retail RI (FRI). Meanwhile, the authors also model a Stackelberg game between e-retailers and manufacturers for analysis. Then, according to customer return behavior and loss aversion, the authors study the optimal pricing decision and RI premium allocation scheme for e-retailers and manufacturers under different scenarios.

It was found that the loss sensitivity reduces customers' willingness to buy RI, which is not conducive to the development of e-retailers and manufacturers. Additionally, with higher loss sensitivity, e-retailers and manufacturers offer FRI to gain higher profits, which supports the implementation of the FRI strategy.

The authors introduce customers' loss aversion into RI to analyze the optimal pricing decisions and profits of e-retailers and manufacturers, enriching the application of loss aversion theory. In addition, this study analyzes the two-way cost-sharing mechanism between manufacturers and e-retailers to provide FRI, which provides a theoretical basis for RI premium sharing.

The purpose of this paper is to investigate the complex interdependence of the factors in driving or hindering construction productivity at the industry, project and activity levels in a systemic manner.

A mixed-methods design, which combines a critical literature review, an interview-based survey with 32 industry experts and five focus group meetings participated in by 109 representatives of a wide range of industry stakeholder groups, was employed to identify the drivers for and constraints on construction productivity enhancement in Hong Kong and explore the interrelated insights into the drivers and constraints.

The study conceptualised and validated a systemic framework for examining construction industry productivity, and developed three causal loop diagrams (CLDs) for illustrating the dynamic structures that underpin the complex systems of the drivers and constraints.

Although the scope of the study was limited to Hong Kong, the results could be interpreted for critical learning in other urban contexts.

The systemic perspective of construction productivity and the CLDs of the drivers and constraints support the systems thinking of industry stakeholders in the formulation of holistic strategies for long-term construction industry productivity enhancement.

The study conceptualises construction productivity from a systemic perspective and provides empirically supported CLDs to facilitate future investigations into the complex system of construction productivity.

With the fierce competition in a knowledge economy, knowledge-intensive enterprises (KIEs) make technological progress in their catching-up processes through implementing product innovation and process innovation. In this study, the aim is to understand the determinants of enterprise innovation type in China's catch-up environment. Further, this paper intends to deal with two related questions. First, what effect does the internal knowledge base have on KIEs' technology innovation activities? Second, considering the technology gap and technology development speed, what are the different impacts of the knowledge base on the type of technology innovation activities?

This paper collected data from 212 KIEs in China through a two-stage questionnaire survey, combined with statistical data for research. The hypothesis was tested by regression analysis. Specifically, descriptive statistics and regression analysis are introduced to test the hypothetical relationship between the knowledge base and technology innovation. Meanwhile, multiple regression is used to test whether there is any difference in the influence of technology gap and technology development speed on enterprise knowledge base and technology innovation. Finally, the corresponding robustness tests are done.

This study finds that in a sample of Yangtze River Delta KIEs, firms' knowledge base influences innovation types. Specifically, the knowledge base width (KBW) and knowledge base depth (KBD) positively influence process innovation, and KBD positively affects product innovation. Regarding the effects of catch-up context factors on KIEs’ innovation choice, a wide technology gap tends to positively influence product innovation in industries with high levels of KBW. Moreover, when technology development speed is high, its potential positive influence on process innovation will be more significant for industries with deeper knowledge bases.

This paper fills the research gap that existing studies ignore the relationship between types of technology innovation and knowledge base dimensions, especially for KIEs. First, this paper deepens the understanding of the impact mechanism of KIEs' existing knowledge base on innovation activities; the unique use of resources by enterprises is the basis of enterprises' competitive advantage and will become enterprises' competitive advantage. Second, this study indicates that against different backdrops of technology gap and technology development speed, enterprises with different knowledge bases will adopt different types of technology innovation activities. Third, this paper shows that a wider technology gap provides broader innovation space, so the technology gap plays a pulling role in KBW and product innovation, thus pushing forward enterprises' technological catch-up.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

The purpose of this study is to enhance the safety and comfort of tourists in scenic areas undergoing renovation and transformation by developing a comprehensive safety assessment model that takes into account both internal and external factors affecting tourist and construction safety.

The research employs a multi-level tourist-construction interaction safety assessment index system, which is constructed through a deep analysis of factors such as the construction environment, tourist behavior and safety signs. The study utilizes game theory in conjunction with three main objective and subjective weight distribution methods to determine the weights of the index system, ensuring the objectivity and effectiveness of the assessment results. The cloud model and cloud generator are applied for the language transformation of the indicators, leading to a comprehensive assessment of construction safety.

The survey results indicate that the safety risks of the case project are relatively high, with limited impact of time segments on safety risks, and the risk level during weekends is slightly higher than on weekdays, but the difference is not significant. Among the reviewed influencing factors, compliance with safety signs and the proportion of people crossing construction areas are the factors with the highest risk level, representing a large number of tourists ignoring safety guidance and forcibly crossing construction areas, facing construction dangers, posing a great challenge to safety management.

This study offers a novel methodological approach to safety risk assessment in similar environments, contributing to the field by improving the systematicness and scientific nature of safety management. It provides a scientific assessment tool for the safety management of tourists in scenic area renovation projects, aiming to achieve the dual objectives of tourist safety and construction efficiency.