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Workplace deviance remains a concern for many organizations, and narcissism has been identified as a primary contributor. The purpose of this paper is to investigate whether political skill and ambition interact with narcissism to attenuate or exacerbate workplace deviance.

The authors surveyed a sample of 335 participants in leadership positions and empirically tested interactions among political skill, narcissism and ambition in predicting workplace deviance.

The authors performed moderated hierarchical regression analyses on the data to test the hypothesis and research question. Contrary to expectations, political skill attenuated the relationship between narcissism and workplace deviance. However, ambition was found to attenuate deviance, with the highest levels of deviance evident when narcissism was high, political skill was low, and ambition was also low.

Although research has examined the relationship between narcissism and workplace deviance, to the authors’ knowledge, the study is the first to examine the roles of political skill and ambition in attenuating the manifestation of narcissism into workplace deviance.

This research aims to explore the integration of Bing Chat, an artificial intelligence (AI)-powered conversational assistant, into academic libraries and its potential impact on reference services.

This study analyzes existing literature to investigate the integration of Bing Chat in academic libraries. This research focuses on its features, capabilities and implications for enhancing user experiences and addressing information needs.

The findings highlight that Bing Chat’s conversational interface facilitates more dynamic and context-aware reference interactions, fostering user empowerment and personalized learning pathways. The integration of Bing Chat with existing library resources enhances resource discovery, navigational assistance and engagement with visual content. However, challenges related to accuracy, privacy and algorithmic bias require careful consideration.

This research contributes to the literature by providing an in-depth exploration of Bing Chat’s integration into academic libraries, emphasizing its transformative potential for reference services. This study addresses ethical and social dimensions associated with AI-driven tools in library environments, offering insights into ensuring fairness, privacy and user trust.

Gives an in depth view of the strategies pursued by the world’s leading chief executive officers in an attempt to provide guidance to new chief executives of today. Considers the marketing strategies employed, together with the organizational structures used and looks at the universal concepts that can be applied to any product. Uses anecdotal evidence to formulate a number of theories which can be used to compare your company with the best in the world. Presents initial survival strategies and then looks at ways companies can broaden their boundaries through manipulation and choice. Covers a huge variety of case studies and examples together with a substantial question and answer section.

Search engine users are faced with long lists of search results, each entry being of a varying degree of relevance. Often users' expectations based on the short text of a search result hold false expectations about the linked web page. This leads users to skip relevant information, missing valuable insights, and click on irrelevant web pages wasting time. The purpose of this paper is to propose a new summary generation technique, ReClose, which combines query‐independent and query‐biased summary techniques to improve the accuracy of users' expectations.

The authors tested the effectiveness of ReClose summaries against Google summaries by surveying 34 participants. Participants were randomly assigned to use one type of summary approach. Summary effectiveness was judged based on the accuracy of each user's expectations.

It was found that individuals using ReClose summaries showed a 10 per cent increase in the expectation accuracy over individuals using Google summaries, and therefore better user satisfaction.

The survey demonstrates the effectiveness of using ReClose summaries to improve the accuracy of user expectations.

This paper presents a novel summary generation technique called ReClose, a new approach to summary evaluation and improvements upon previously proposed summary generation techniques.

This is the third of a series of studies on Chinese brand naming using content analysis from a linguistic perspective. The first study generalized the principles guiding Chinese brands in terms of syllable pattern, tone pattern, compounding structure and semantic preference. The second looked at specific branding rules, focusing on two entirely different products: cosmetic products and bicycles. The present study, following the same linguistic framework of analysis, analyzes three groups of closely related products: spirits, beers, soft drinks, to see how these brands are creatively and distinctively constructed. Finds that the brand naming patterns of the three drinks are basically in agreement with the general Chinese branding principles, and the differences among them directly reflect the development, the consumer markets and characteristics of each product.

On-orbit service technology is one of the key technologies of space manipulation activities such as spacecraft life extension, fault spacecraft capture, on-orbit debris removal and so on. It is known that the failure satellites, space debris and enemy spacecrafts in space are almost all non-cooperative targets. Relatively accurate pose estimation is critical to spatial operations, but also a recognized technical difficulty because of the undefined prior information of non-cooperative targets. With the rapid development of laser radar, the application of laser scanning equipment is increasing in the measurement of non-cooperative targets. It is necessary to research a new pose estimation method for non-cooperative targets based on 3D point cloud. The paper aims to discuss these issues.

In this paper, a method based on the inherent characteristics of a spacecraft is proposed for estimating the pose (position and attitude) of the spatial non-cooperative target. First, we need to preprocess the obtained point cloud to reduce noise and improve the quality of data. Second, according to the features of the satellite, a recognition system used for non-cooperative measurement is designed. The components which are common in the configuration of satellite are chosen as the recognized object. Finally, based on the identified object, the ICP algorithm is used to calculate the pose between two frames of point cloud in different times to finish pose estimation.

The new method enhances the matching speed and improves the accuracy of pose estimation compared with traditional methods by reducing the number of matching points. The recognition of components on non-cooperative spacecraft directly contributes to the space docking, on-orbit capture and relative navigation.

Limited to the measurement distance of the laser radar, this paper considers the pose estimation for non-cooperative spacecraft in the close range.

The pose estimation method for non-cooperative spacecraft in this paper is mainly applied to close proximity space operations such as final rendezvous phase of spacecraft or ultra-close approaching phase of target capture. The system can recognize components needed to be capture and provide the relative pose of non-cooperative spacecraft. The method in this paper is more robust compared with the traditional single component recognition method and overall matching method when scanning of laser radar is not complete or the components are blocked.

This paper introduces a new pose estimation method for non-cooperative spacecraft based on point cloud. The experimental results show that the proposed method can effectively identify the features of non-cooperative targets and track their position and attitude. The method is robust to the noise and greatly improves the speed of pose estimation while guarantee the accuracy.

One of the under-explored aspects in the process of user information seeking behaviour is influence of time on relevance evaluation. It has been shown in previous studies that individual users might change their assessment of search results over time. It is also known that aggregated judgements of multiple individual users can lead to correct and reliable decisions; this phenomenon is known as the “wisdom of crowds”. The purpose of this paper is to examine whether aggregated judgements will be more stable and thus more reliable over time than individual user judgements.

In this study two simple measures are proposed to calculate the aggregated judgements of search results and compare their reliability and stability to individual user judgements. In addition, the aggregated “wisdom of crowds” judgements were used as a means to compare the differences between human assessments of search results and search engine’s rankings. A large-scale user study was conducted with 87 participants who evaluated two different queries and four diverse result sets twice, with an interval of two months. Two types of judgements were considered in this study: relevance on a four-point scale, and ranking on a ten-point scale without ties.

It was found that aggregated judgements are much more stable than individual user judgements, yet they are quite different from search engine rankings.

The proposed “wisdom of crowds”-based approach provides a reliable reference point for the evaluation of search engines. This is also important for exploring the need of personalisation and adapting search engine’s ranking over time to changes in users preferences.

This is a first study that applies the notion of “wisdom of crowds” to examine an under-explored in the literature phenomenon of “change in time” in user evaluation of relevance.

The purpose of this paper is to introduce a summarization method to enhance the current web-search approaches by offering a summary of each clustered set of web-search results with contents addressing the same topic, which should allow the user to quickly identify the information covered in the clustered search results. Web search engines, such as Google, Bing and Yahoo!, rank the set of documents S retrieved in response to a user query and represent each document D in S using a title and a snippet, which serves as an abstract of D. Snippets, however, are not as useful as they are designed for, i.e. assisting its users to quickly identify results of interest. These snippets are inadequate in providing distinct information and capture the main contents of the corresponding documents. Moreover, when the intended information need specified in a search query is ambiguous, it is very difficult, if not impossible, for a search engine to identify precisely the set of documents that satisfy the user’s intended request without requiring additional information. Furthermore, a document title is not always a good indicator of the content of the corresponding document either.

The authors propose to develop a query-based summarizer, called Q_Sum, in solving the existing problems of Web search engines which use titles and abstracts in capturing the contents of retrieved documents. Q_Sum generates a concise/comprehensive summary for each cluster of documents retrieved in response to a user query, which saves the user’s time and effort in searching for specific information of interest by skipping the step to browse through the retrieved documents one by one.

Experimental results show that Q_Sum is effective and efficient in creating a high-quality summary for each cluster to enhance Web search.

The proposed query-based summarizer, Q_Sum, is unique based on its searching approach. Q_Sum is also a significant contribution to the Web search community, as it handles the ambiguous problem of a search query by creating summaries in response to different interpretations of the search which offer a “road map” to assist users to quickly identify information of interest.

Wine sales are at the highest volumes ever and warrant a robust understanding of consumption behavior. Consequently, this study aims to examine intrinsic motivational factors (e.g. personal attributes) – those that push the consumer toward wine products – and the extrinsic motivational product attributes (e.g. situational attributes) those that draw – or pull – the consumer toward wine products.

A model has been tested in which intrinsic and extrinsic motivations interacted to predict personal involvement (PI) with wine, which in turn predicted wine consumption, forming a mediated moderation model.

Support has been found for a mediated moderation model of wine consumption. Thus, this study improves the understanding of how interactive motivations are mediated by PI in their influence on wine consumption.

The sample is limited to participants in the National Restaurant Show, and thus the results may be limited to the sample investigated.

The findings suggest using intrinsic and extrinsic motivational factors and PI with wine to influence marketing strategies.

This study has helped to expand the understanding of interactive and mediating forces that drive wine consumption.

Although previous research proposed that motivational factors interact to predict wine consumption, this interaction has not been tested empirically prior to the current study. Therefore, this study adds new insights into wine consumption by demonstrating that intrinsic and extrinsic motivators interact to predict PI with wine, which subsequently predicts wine consumption in a holistic, mediated moderation model.

The purpose of this paper is to determine how high-tech firms should choose between independent research and development and technology introduction as well as to ascertain the effects of the three elements of competitive dynamics on the evolution of innovative behavior-based decisions and competitive results.

This paper describes the construction of an evolutionary game model and a multi-agent-based model of innovative behavior-based decisions by heterogeneous high-tech firms. The models are used to analyze the evolution path and evolutionarily stable strategy of innovative behavior-based decisions. In addition, multi-agent-based simulation is used to gain insight into the effects of competitive dynamics on the dynamic evolution of innovative behavior-based decisions.

This paper reveals four evolutionary equilibrium states of the innovation behavior-based decisions of high-tech firms. Based on the findings, these overall evolutionary trends are not affected by the timing of competitive market entry or the intensity of competition. In addition, simulated evidence is added that the timing of competitive market entry is an important factor affecting market-leading innovative strategies and dynamic competition results, and competition intensity is closely related to the evolutionary speed of innovation behavior-based decisions.

The key contribution of this paper is its new view of innovative behavior-based decisions from a competitive dynamics perspective. The new competitive dynamics-based framework for innovative behavior-based decisions of high-tech firms proposed in the paper can resolve the problem of obtaining a sustainable competitive advantage for high-tech firms in a competitive dynamics context.