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The effect of different formats of message delivery has received little theoretical and empirical examination. This research focuses on the effect of written relational communication formats used by service providers. This study aims to answer three questions: Do different formats of written communications (i.e. handwriting and print) influence customer perceptions (i.e. feelings of warmth) of service firms? What are the mediators of these influences (i.e. perceived effort and psychological closeness)? And under what conditions do they occur (i.e. what is the contextual factor)?

One field study and three laboratory studies were conducted to provide a comprehensive understanding of the role of format in written communication.

Handwritten messages are more effective than print messages in building relationships in a service context because they elicit stronger feelings of warmth because of both the perception of greater effort and feelings of greater psychological closeness to the service provider. However, the presence of handwriting fails to deliver feelings of warmth when the quality of core services is low.

Service providers can effectively use handwritten communication to signal effort and create psychological closeness for relationship building with their key customers only when the quality of core services meets customer expectations.

First, the research differentiates the formats of written relational communication (handwritten vs print), and links communication formats with feelings of warmth, which is an important factor for impression and relationship formation in the practice of services marketing. Second, based on cognitive-experiential self-theory, this research demonstrates the dual mediators underlying the effect of handwriting (vs print) on warmth: perceived effort and psychological closeness. Third, it identifies the quality of core service as a boundary condition for the effect of handwritten communication.

The purpose of this paper is to explore the two effects (flow effect and resonance effect) during a group complaint based on the emotional contagion theory.

This study uses an experimental research design in which participants’ negative emotions dynamically change driven by group emotional interactions when they are experiencing a group complaint.

Flow effect and resonance effect can occur during the process of group emotional contagion. Specifically, when group customers’ negative emotional similarity is low in a group complaint, group emotional contagion leads to flow effect (i.e. negative emotions flow from customers with higher levels of negative emotions to those with lower levels of negative emotions). By contrast, when group customers’ negative emotional similarity is high in a group complaint, group emotional contagion leads to resonance effect (i.e. group customers’ negative emotions increase significantly).

Most of the previous research studies the process of emotional contagion from one with higher levels of emotional displays to the other with lower levels of emotional displays, which is named as the “flow effect” of emotional contagion. However, when two individuals with the same levels of negative emotional displays interact with each other, the flow effect of emotional contagion is very likely not to occur. It is interesting to find that both individuals’ negative emotions increase significantly during the process of emotional contagion. The authors propose the “resonance effect” of emotional contagion to explain this phenomenon.

The purpose of this paper is to examine the interaction effect of innovative product category and presentation order on consumer consumer’s purchasing intention and the mediating role of perceived novelty and risk perception.

The authors examined the hypotheses in three experiment studies. In Study 1, the authors primed innovative product category and presentation order on consumer consumer’s purchasing intention. In Study 2, the authors measured the mediating role of perceived novelty and risk perception. In Study 3, they validated the moderating effect of picture and text consistency on the improvement of purchase preference.

The results reveal that RNP/INP and presentation order (from whole to part/from part to whole) could enhance consumers’ purchase intention and verify the mediating role of perceived novelty and risk perception, based on which a complete internal mechanism model is constructed. The third experiment shows the moderating effect of picture and text consistency on the improvement of purchase preference by matching the category and presentation order of innovative products.

Prior literature on the thinking mode of holistic and partial processing has been mostly applied to the cognitive field of reading and text labeling. In this study, using the holistic (local) processing thinking model and anchoring theory, eye movement experiments and situational experiments, the audience’s analysis framework of information processing mechanism is constructed. The unique phenomenon of product category and overall (local) presentation order coexisting in innovative product advertisement is considered comprehensively.

The purpose of this paper is to examine the influence of transparency on consumers' judgment and decision-making.

This study uses an experimental research design in which participants' negative emotions dynamically change driven by group emotional interactions when they are experiencing a group complaint.

The experimental results show that compared with opaque products, transparent products make consumers rely more on emotions to make judgments and decisions (Experiment 1). It is precise because transparency increases the influence of emotion on consumers' judgment and decision-making that positive emotion makes consumers' evaluation and willingness to pay higher, while negative emotion makes consumers' evaluation and willingness to pay lower (Experiments 2 and 3). Transparency will also affect consumers' subsequent judgment and decision-making methods, so they are more inclined to choose the option with the dominant emotional dimension (Experiment 4).

Previous studies mainly focus on the impact of transparent packaging on consumers and discuss the impact of transparent packaging on consumer product evaluation and consumption quantity. This study proves that product-related transparent elements can also affect consumers' decision-making methods, making them more dependent on emotions to make decisions, enriching the research on the influencing factors of consumer decision-making methods.

The concept of face has been deeply embedded in Chinese culture. This paper aims to examine the role of face in service failure and recovery encounters, and to explore the factors that influence customer emotions, recovery satisfaction and behavioral intention based on a proposed conceptual model.

The paper uses a scenario role‐play‐based experimental design to examine the proposed conceptual model in service failure and recovery encounters.

The results show that face plays an important role in service failure and recovery encounters. Specifically, it is found that the utilitarian recovery and symbolic recovery customers receive in the service recovery encounter can both influence customer face restoration, which in turn affects customer emotions, and behavioral intention on service recovery. Customer emotions are significantly correlated to customer recovery satisfaction.

The practical implications of this study are mainly two‐fold. First, service providers should pay attention to customers' perceptions of face during service encounters. They should also train their employees to treat all customers with utmost sincerity, which will help to enhance the positive emotional experience of customers. Second, the recovery offered by service providers should not be limited to utilitarian recovery. It has to take into account symbolic recovery as well.

This paper investigates the role of face in service recovery encounters and suggests that face significantly influences customer emotions and behavioral intention on service recovery, and customer emotions in turn significantly affect customer recovery satisfaction

The purpose of this paper is to optimize the structure of plough blades in a ploughshare mixer using the discrete element method (DEM) simulations.

Using the validated DEM model, three numerical tests are conducted to determine how the mixing performance evolves as structural parameters of blades change. Results from the analysis provide basis for structure optimization of blades. The structural parameters include sweep angle of blade γ, regular axial pitch p and regular circumferential angular offset α. The parameters to evaluate mixing performance include mass flow rate and Lacey index.

The DEM results show that the mixing performance at γ of 35° is better than 15°, 25° and 45°. The mixer which has a p of less than or equal to 1.11 · b is more efficient than the mixer which has a p greater than 1.11 · b, where b is tail width of blade. The circumferential symmetric distribution of blades (α = 180°) is more beneficial to improve the mixing performance in comparison with the circumferential asymmetric distribution (α < 180°). Based on the results, an optimized mixer with a γ of 35°, a p of 0.61 · b and an α of 180° is proposed, which has a better mixing performance compared to all mixers listed.

The structural parameters of blades, including γ, p and α, are found to be critical for good mixing. From the view angle of structure optimization of plough blades, a new ploughshare mixer with a γ of 35°, a p of 0.61 · b and an α of 180° is investigated and recommended for improving mixing efficiency.

Urban water bodies play an important role in reducing summertime urban heat island (UHI) effects. Previous studies focused mainly on the impact of water bodies of large areas, and there is no analysis of the efficacy and scale effect of how small and medium-sized water bodies reduce the UHI effects. Hence, these studies could not provide theoretical support for the scientific planning and design of urban water bodies. This study aims to confirm, within different scale ranges, the efficacy of a water body in reducing the summertime UHI effects. We propose a scale sensitivity method to investigate the temporal and spatial relationship between urban water bodies and UHI. Based on the scale theory and geostatistical analysis method in landscape ecology, this study used the platforms of 3S, MATLAB, and SPSS to analyze the distance-decay law of water bodies in reducing summertime UHI effects, as well as the scale response at different water surface ratios. The results show that the influence of water surfaces on UHIs gradually decreases with increasing distance, and the temperature rises by 0.78 °C for every 100 m away from the water body. During daytime, there is a scaled sensitivity of how much water surfaces reduce the summertime UHI effects. The most sensitive radius from the water was found at the core water surface ratio of 200 m. A reduction of UHI intensity by 2.3 °C was observed for every 10% increase of the average core water surface ratio. This study provides a theoretical reference to the control of heat islands for the planning and design of urban water bodies.

This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice.

This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs.

To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required.

This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

This study looked into the factors that could make a difference in teachers’ individual innovativeness and team innovativeness. We investigated five categories of factors: (1) innovation-related teacher preparedness, (2) innovation-related teacher professional development, (3) teacher professional practices, (4) teacher empowerment and (5) innovation-related teacher self-efficacy.

The data source is the 2018 Teaching and Learning International Survey (TALIS) US data. The samples include about 165 schools and 2,560 teachers. We applied the structural equation model to analyze the data and the unit of analysis is set at the individual teacher-level.

We found that all factors matter except professional development and that they matter differently for different innovativeness outcomes.

This study is significant in several aspects: first, it is among the first that examined the factors that could make a difference in teacher innovativeness. Second, we differentiated between individual and team teacher innovativeness. Third, the findings highlight the importance of several factors including teacher preparation, teacher collaboration, teacher participation in school decisions and teacher self-efficacy.

MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity countermeasure domain, such as dynamic, emulated and file analysis. Those entities are linked by applying relationships such as analyze, may_contains and encrypt. A fundamental challenge for collaborative designers is to encode knowledge and efficiently interrelate the cyber-domain facts generated daily. However, the designers manually update the graph contents with new or missing facts to enrich the knowledge. This paper aims to propose an automated approach to predict the missing facts using the link prediction task, leveraging embedding as representation learning.

D3FEND is available in the resource description framework (RDF) format. In the preprocessing step, the facts in RDF format converted to subject–predicate–object triplet format contain 5,967 entities and 98 relationship types. Progressive distance-based, bilinear and convolutional embedding models are applied to learn the embeddings of entities and relations. This study presents a link prediction task to infer missing facts using learned embeddings.

Experimental results show that the translational model performs well on high-rank results, whereas the bilinear model is superior in capturing the latent semantics of complex relationship types. However, the convolutional model outperforms 44% of the true facts and achieves a 3% improvement in results compared to other models.

Despite the success of embedding models to enrich D3FEND using link prediction under the supervised learning setup, it has some limitations, such as not capturing diversity and hierarchies of relations. The average node degree of D3FEND KG is 16.85, with 12% of entities having a node degree less than 2, especially there are many entities or relations with few or no observed links. This results in sparsity and data imbalance, which affect the model performance even after increasing the embedding vector size. Moreover, KG embedding models consider existing entities and relations and may not incorporate external or contextual information such as textual descriptions, temporal dynamics or domain knowledge, which can enhance the link prediction performance.

Link prediction in the D3FEND KG can benefit cybersecurity countermeasure strategies in several ways, such as it can help to identify gaps or weaknesses in the existing defensive methods and suggest possible ways to improve or augment them; it can help to compare and contrast different defensive methods and understand their trade-offs and synergies; it can help to discover novel or emerging defensive methods by inferring new relations from existing data or external sources; and it can help to generate recommendations or guidance for selecting or deploying appropriate defensive methods based on the characteristics and objectives of the system or network.

The representation learning approach helps to reduce incompleteness using a link prediction that infers possible missing facts by using the existing entities and relations of D3FEND.