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The purpose of this article is to explore the relationship between the attributes of Olympic mascots and their impact on sponsorship effectiveness. Based on a multiattribute model and the introduction of engagement theory and the meaning transfer model, this article uses the 2022 Beijing Winter Olympics mascot “Bing Dwen Dwen” as the research object to empirically analyze the effects and mechanisms of the mascot's attributes on preference, event engagement, sponsorship enterprise trust and sponsorship enterprise attitude, ultimately constructing a sponsorship effectiveness model.

The survey method was used to examine 238 respondents' emotions and attitudes towards companies participating in sponsoring Olympic mascots.

The study found that the main attributes of the mascot include visual and emotional factors, both of which have a positive impact on preference, with emotional factors having a greater influence than visual factors. Visual and emotional factors indirectly affect engagement through preference. Preference and engagement play a completely mediating role in the effect of mascot attributes on sponsorship enterprise trust and sponsorship enterprise attitude.

This study provides practical recommendations for managers to achieve marketing success in sports sponsorship through mascots.

This paper provides a measurement tool for the study of mascot attributes and important support for subsequent research in sponsorship marketing.

The purpose of this study is to provide a series of observations to help information professionals understand and anticipate the future developments.

This column delves into artificial intelligence (AI)-powered search and its implications for the open Web to show how multifaceted the impact of generative text AI might be in a single arena.

This framework includes the following observations: future applications are unpredictable, interdependent and will require flexibility, AI’s ability to create the feeling of intelligence combined with the power of language are key to its future; the technology is not neutral, it has biases and significant limitations; its widespread adoption will come with trade-offs that matter; and that libraries should develop a positive vision for how to use it.

For libraries to serve their patrons or embody their values, they must be prepared to grapple with a rapidly evolving technology by understanding the technologies possibilities, what makes it compelling, as well as its limitations and dangers.

This paper aims to study a new halogen-free fame-retardant curing agent 1-aminoethylidenediphosphonate (AAEDP).

The AAEDP was synthesized by phosphoric acid, acetonitrile and ammonia. The chemical structures of AAEDP were characterized by proton nuclear magnetic resonance, mass spectrometry and Fourier transform infrared spectrometer. Thermal gravimetric analysis (TGA) and scanning electron microscope (SEM) would study the thermal properties and the char residues of AAEDP/EP. The thermal stability, mechanical and flame properties and morphology for the char layer of composite materials were separately investigated using TGA, tensile and charpy impact tests, limiting oxygen index (LOI), UL-94 HB flammability standard (UL-94) and SEM.

The results showed that the AAEDP had been prepared successfully. When the intumescent flame retardant was added into the EP, the LOI of composite material was improved.

The AAEDP can be prepared successfully and can improve the flame resistance of composite material.

The AAEDP has excellent flame-retardant properties and produce no toxic fumes when burnt in case of fire.

The results showed that the phosphorus content of AAEDP was 2.958 Wt.%; the impact and tensile strength of the composite material were 6.417 kJ m⁻² and 38.0 MPa, respectively; and the LOI and UL-94 were 29.7% and V-0 ranking, respectively. The TGA results indicated that the carbon residue ratio can be increased by 1000°C in air. The denser and more uniform structure of residual carbon prevents heat transfer and diffusion, restricts the production of combustible gas and reduces the rate of heat release.

This paper aims to give an overview of the history and evolution of commercial search engines. It traces the development of search engines from their early days to their current form as complex technology-powered systems that offer a wide range of features and services.

In recent years, advancements in artificial intelligence (AI) technology have led to the development of AI-powered chat services. This study explores official announcements and releases of three major search engines, Google, Bing and Baidu, of AI-powered chat services.

Three major players in the search engine market, Google, Microsoft and Baidu started to integrate AI chat into their search results. Google has released Bard, later upgraded to Gemini, a LaMDA-powered conversational AI service. Microsoft has launched Bing Chat, renamed later to Copilot, a GPT-powered by OpenAI search engine. The largest search engine in China, Baidu, released a similar service called Ernie. There are also new AI-based search engines, which are briefly described.

This paper discusses the strengths and weaknesses of the traditional – algorithmic powered search engines and modern search with generative AI support, and the possibilities of merging them into one service. This study stresses the types of inquiries provided to search engines, users’ habits of using search engines and the technological advantage of search engine infrastructure.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

The speed of integration has been a salient and longstanding topic in the literature on managing mergers and acquisitions. Yet over the decades, speed has also been the subject of extensive debate. While many have advocated for fast integration, others have recommended a more measured pace. In this chapter, the authors reflect on the discussion by canvasing the variety of views on the speed of integration. The work is positioned at the nexus of the literature on mergers with that on stakeholders, in particular its attention to urgency in stakeholder management. It approaches urgency in mergers and acquisitions as a “dilemma of stake,” a new lens on a well-established but challenging topic. The study draws on ethnographic research to examine accounts of speed of integration in a New Zealand public sector merger. The chapter juxtaposes varied views on the topic against the respective arguments within the merger literature. It examines the overarching themes of “go slow” and the “need for speed” by attending to the tensions between a prosocial service ethos on the one hand and a managerialist ethos on the other. The explication of the respective dilemmas of stake shows how participants articulate their views on urgency both in terms of its effects on their individual professional role, their own stake, as well as in terms of the effects on employees as internal stakeholders. The analysis also explores the role of internal and external context in shaping the views on urgency in merger integration. The work concludes by outlining an agenda for future research.

This paper applies a volume-price probability wave differential equation to propose a conceptual theory and has innovative behavioral interpretations of intraday dynamic market equilibrium price, in which traders' momentum, reversal and interactive behaviors play roles.

The authors select intraday cumulative trading volume distribution over price as revealed preferences. An equilibrium price is a price at which the corresponding cumulative trading volume achieves the maximum value. Based on the existence of the equilibrium in social finance, the authors propose a testable interacting traders' preference hypothesis without imposing the invariance criterion of rational choices. Interactively coherent preferences signify the choices subject to interactive invariance over price.

The authors find that interactive trading choices generate a constant frequency over price and intraday dynamic market equilibrium in a tug-of-war between momentum and reversal traders. The authors explain the market equilibrium through interactive, momentum and reversal traders. The intelligent interactive trading preferences are coherent and account for local dynamic market equilibrium, holistic dynamic market disequilibrium and the nonlinear and non-monotone V-shaped probability of selling over profit (BH curves).

The authors will understand investors' behaviors and dynamic markets through more empirical execution in the future, suggesting a unified theory available in social finance.

The authors can apply the subjects' intelligent behaviors to artificial intelligence (AI), deep learning and financial technology.

Understanding the behavior of interacting individuals or units will help social risk management beyond the frontiers of the financial market, such as governance in an organization, social violence in a country and COVID-19 pandemics worldwide.

It uncovers subjects' intelligent interactively trading behaviors.

Despite mounting evidence that job insecurity triggers counterproductive work behavior (CWB), the underlying mechanism within the association in hybrid employment remains unknown. This study aims to investigate turnover intention as a mediator in the association between job insecurity and CWB, as well as the differences between permanent and contract workers concerning the effect of job insecurity on employees’ CWB.

Dyadic data were collected from 213 workers (103 contract and 110 permanent workers) and their supervisors, who were employed in one of the three branches of a Chinese state-owned enterprise, respectively, located in Hunan, Shenyang and Xinjiang.

This study found that job insecurity was positively related to CWB, in which turnover intention acted as a partial mediator. Employment status acted as a moderator between job insecurity and CWB.

First, this study extends the theoretical knowledge concerning how job insecurity activates CWB by identifying turnover intention as a mediating mechanism. To the best of the authors’ knowledge, this is the first study to explore the mediating role of turnover intention in terms of job insecurity and CWB. Second, this research expands the understanding of the relation between job insecurity and CWB by investigating this link in the case of contract workers versus permanent workers. Finally, this paper aims to provide an understanding of why contract workers and permanent workers may differ in their reactions to job insecurity.

Previous studies have shown that the application of information technology (IT) can help break through the innovation boundaries of firms and has undoubtedly become a key enabler of collaborative innovation. These studies, however, are mainly based on theoretical analysis and case studies, and little is empirically known about the relationship between IT investments and collaborative innovation. Therefore, the purpose of this study is to empirically explore how firms' IT investments affect the firms' collaborative innovation performance. The authors also examine the moderating roles of the top management team's (TMT's) educational background and absorptive capacity in this relationship.

The authors collected data on 2,097 listed Chinese manufacturing companies and used the ordinary least squares (OLS) method to perform regression analysis. In addition, the authors conducted robustness tests using the propensity score matching (PSM) method and the instrumental variable method.

The results show that the relationship between IT investments and collaborative innovation is inverted, U-shaped and curvilinear. In addition, the TMT's educational background and absorptive capacity positively moderate the inverted U-shaped relationship between IT investments and collaborative innovation.

The study's findings on the relationship between IT investments and collaborative innovation differ from previous mainstream findings that recognized a positive linear relationship. The authors' findings deepen the understanding of the dual role of IT investments. Moreover, this research helps expand the contingency perspective in IT investments and collaborative innovation research.