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With the vigorous development of the e-commerce delivery service industry, delivery service has become an important factor for e-tailers to obtain the market competitive advantage. However, how to choose the best delivery service strategy is a difficult problem for e-tailers in practice. The purpose of this paper is to investigate the effect of delivery service on e-tailers and online platforms.

Based on the Stackelberg game, the research study establishes and solves three models, namely dual self-supporting delivery service model, dual third-party delivery service model and differential delivery service model.

The results show that when the self-supporting and third-party delivery cost coefficients are all small, no matter which delivery service providers the competitor selects, the e-tailer selects delivery with a lower service fee. When the self-supporting and third-party delivery service fee are all low, no matter which delivery service providers the competitor selects, the e-tailer selects delivery with a smaller service cost. Both service fee and service cost determine the choice of e-tailers' delivery strategy. When the commission rate is moderate, both e-tailers are willing to choose the self-supporting delivery strategy, but the platform only prefers to provide self-supporting delivery to them with a lower delivery service sensitivity coefficient.

This paper analyzes the optimal delivery service strategies for e-tailers to compete with competitors, and explores the impacts of parameters for e-tailers and online platforms in their decision-making. The findings provide valuable implications for relevant practices.

Based on the principal–agent and stakeholder theories, this study aims to put forward an intermediary model to verify the intermediary role of corporate social responsibility (CSR) in executive equity incentives and corporate innovation performance to improve corporate innovation performance.

The 2012–2018 A-share listed companies’ disclosure of executive equity incentives data was used as the research sample. This study used CSR as an intermediary to explore the relationship between executive equity incentives and corporate innovation performance. A verification analysis was carried out.

The research results show that: a positive correlation exists between executive equity incentives and corporate innovation performance, and executives’ reasonable equity incentives can promote the growth of corporate innovation performance. A positive correlation exists between executive equity incentives and CSR. Implementing equity incentives for executives can stimulate their motivation to assume CSR. A positive correlation exists between CSR and corporate innovation performance. The more a company fulfills its social responsibility, the more it can promote the improvement of corporate innovation performance. CSR plays a mediating role between executive equity incentives and corporate innovation performance. CSR promotes executive equity incentives’ impact on corporate innovation performance and exerts a “complete mediating effect” between the two.

The number of samples and the time span of samples can be expanded in the future. This research has tested the mediating effect of CSR, but other mediating variables may play a role in the process of executive equity incentives in promoting corporate innovation performance. Further research should be conducted to explore the mediating effect of financing constraints and media attention on corporate innovation performance. This study only verifies the influence of equity incentives on CSR and innovation performance of senior executives. In the future, other incentive methods should be explored, such as salary incentives.

Foreign research on equity incentives has matured, but the experience of foreign countries cannot necessarily produce the expected effect in China. More than ten years have passed since the China A-share market began implementing equity incentives on December 31, 2005. As of December 31, 2017, about one-third of enterprises in the high-tech industry that had introduced equity incentives had stopped implementing the policy. Data from 2012 to 2018 were selected to analyze the relationship between executive equity incentives, CSR and corporate innovation performance to explore the influence mechanism of equity incentives. This study provides a comprehensive theoretical framework to examine the interaction among executive equity incentives, CSR and corporate innovation performance. Because most previous studies have focused on the relationship between executive equity incentives, CSR and corporate innovation performance, they are rarely been used as an intermediary variable to explore the impact of executive equity incentives on corporate innovation performance. This study explores the impact of executive equity incentives on corporate innovation performance under the influence of CSR. Moreover, this study explores the mediating role of CSR in corporate governance, which provides a new perspective for CSR research and verifies relevant literature on the mediating effect model.

Research countermeasures and suggestions: the research results are significant for enterprises implementing executive equity incentives, fulfilling CSR, enhancing corporate reputation, improving corporate innovation performance and ultimately obtaining market competitiveness. Therefore, the following suggestions are proposed: establish and improve the executive equity incentive mechanism and strengthen the promotion effect of executive equity incentives in CSR and corporate innovation performance. Strengthen the awareness of enterprises to actively fulfill CSR and give full play to the role of CSR in promoting corporate innovation performance. Improve the profitability of enterprises and focus on the promotion effect of enterprise profitability on corporate innovation performance.

This study focuses on executive equity incentives and introduces CSR as an intermediary variable to explore the influence path of executive equity incentives on corporate innovation performance. Based on the research results, this study takes targeted measures to improve corporate innovation performance and maintain its healthy growth of corporate innovation performance. This is significant in enhancing enterprises’ core competitiveness and promoting the enterprise economy’s sustainable development. Meanwhile, the enterprise has significant reference value in actively fulfilling its CSR and realizing its stable and healthy development.

Stainless-clad bimetallic steels (SCBS) are widely investigated in some extremely environmental applications areas, such as polar sailing area and tropical oil and gas platforms areas, because of their excellent anticorrosion performance and relatively lower production costs. However, the properties of SCBS, including the mechanical strength, weldability and the anticorrosion behavior, have a direct relation with the manufacturing process and can affect their practical applications. This paper aims to review the application and the properties requirements of SCBS in marine environments to promote the application of this new material in more fields.

In this paper, the manufacturing process, welding and corrosion-resistant properties of SCBS were introduced systematically by reviewing the related literatures, and some results of the authors’ research group were also introduced briefly.

Different preparation methods, such as rolling composite, casting rolling composite, explosive composite, laser cladding and plasma arc cladding, as well as the process parameters, including the vacuum degree, rolling temperature, rolling reduction ratio, volume ratios of liquid to solid, explosive ratio and the heat treatment, influenced a lot on the properties of the SCBS through changing the interface microstructures. Otherwise, the variations in rolling temperature, pass, reduction and the grain size of clad steel also brought the dissimilarities of the mechanical properties, microhardness, bonding strength and toughness. Another two new processes, clad teeming method and interlayer explosive welding, deserve more attention because of their excellent microstructure control ability. The superior corrosion resistance of SCBS can alleviate the corrosion problem in the marine environment and prolong the service life of the equipment, but the phenomenon of galvanic corrosion should be noted as much as possible. The high dilution rate, welding process specifications and heat treatment can weaken the intergranular corrosion resistance in the weld area.

This paper summarizes the application of SCBS in marine environments and provides an overview and reference for the research of stainless-clad bimetallic steel.

Social commerce (s-commerce) has gained widespread popularity as a social platform where customers engage in resource-sharing activities such as information exchange, advice-seeking and expressing their opinions on mutual interests. However, existing studies have not fully comprehended the drivers of electronic customer-to-customer interaction (eCCI) and how such behavior contributes to the customer “stick” on s-commerce sites. This study develops the Motivation–Opportunity–Ability (MOA) theory and investigates the impact of MOA factors on eCCI, which in turn affects customer stickiness.

A survey was used to acquire data from 455 valid respondents, and the research employed a combination of fuzzy-set qualitative comparative analysis (fsQCA) and structural equation modeling.

The results revealed associations between perceived self-efficacy, intrinsic motivation, tie strength with other customers, eCCI and customer stickiness.

Considering the limited availability of complete eCCI frameworks in existing scholarly works, the authors present valuable perspectives on the role of consumer characteristics as both antecedents and consequences of eCCI. Additionally, this study proposes a research agenda for the field of eCCI on s-commerce sites.

Currently, the Tang tomb mural cultural relic resources are presented in a multi-source and heterogeneous manner, with a lack of effective organization and sharing between resources. Therefore, this study aims to propose a multidimensional knowledge discovery solution for Tang tomb mural cultural relic resources.

Taking the Tang tomb murals collected by the Shaanxi History Museum as an example, based on clarifying the relevant concepts of Tang tomb mural resources and considering both dynamic and static dimensions, a top-down approach was adopted to first construct an ontology model of Tang tomb mural type cultural relics resources. Then, the actual case data was imported into the Neo4J graph database according to the defined pattern hierarchy to complete the static organization of knowledge, and presented in a multimodal form in knowledge reasoning and retrieval. In addition, geographic information system (GIS) technology is used to dynamically display the spatiotemporal distribution of Tang tomb mural resources, and the distribution trend is analysed from a digital humanistic perspective.

The multi-dimensional knowledge discovery of Tang tomb mural cultural relics resources can help establish the correlation and spatiotemporal relationship between resources, providing support for semantic retrieval and navigation, knowledge discovery and visualization and so on.

This study takes the murals in the collection of the Shaanxi History Museum as an example, revealing potential knowledge associations in a static and intelligent way, achieving knowledge discovery and management of Tang tomb murals, and dynamically presents the spatial distribution of Tang tomb murals through GIS technology, meeting the knowledge presentation needs of different users and opening up new ideas for the study of Tang tomb murals.

1) The OE-MLPG penalty meshfree method is developed to solve cracked structure.(2) Smartening the numerical meshfree method by combining the particle swarm optimization (PSO) optimization algorithms and Voronoi computational geometric algorithm. (3). Selection of base functions, finding optimal penalty factor and distribution of appropriate nodal points to the accuracy of calculation in the meshless local Petrov–Galekrin (MLPG) meshless method.

Using appropriate shape functions and distribution of nodal points in local domains and sub-domains and choosing an approximation or interpolation method has an effective role in the application of meshless methods for the analysis of computational fracture mechanics problems, especially problems with geometric discontinuity and cracks. In this research, computational geometry technique, based on the Voronoi diagram (VD) and Delaunay triangulation and PSO algorithm, are used to distribute nodal points in the sub-domain of analysis (crack line and around it on the crack plane).

By doing this process, the problems caused by too closeness of nodal points in computationally sensitive areas that exist in general methods of nodal point distribution are also solved. Comparing the effect of the number of sentences of basic functions and their order in the definition of shape functions, performing the mono-objective PSO algorithm to find the penalty factor, the coefficient, convergence, arrangement of nodal points during the three stages of VD implementation and the accuracy of the answers found indicates, the efficiency of V-E-MLPG method with Ns = 7 and ß = 0.0037–0.0075 to estimation of 3D-stress intensity factors (3D-SIFs) in computational fracture mechanics.

The present manuscript is a continuation of the studies (Ref. [33]) carried out by the authors, about; feasibility assessment, improvement and solution of challenges, introduction of more capacities and capabilities of the numerical MLPG method have been used. In order to validate the modeling and accuracy of calculations, the results have been compared with the findings of reference article [34] and [35].

This study aims, first, to understand consumers’ perception of chefs as human brands (i.e. study one). Second, tests were run to assess the validity of a new conceptual model of the relationships between the factors of chef image, luxury restaurant image, both images’ congruity and consumers’ hedonic and novelty experiences and happiness and well-being (i.e. study two).

The first qualitative study involved using Leximancer software to analyse the data drawn from 43 interviews with luxury restaurant clients. In the second quantitative study, 993 valid survey questionnaires were collected, and the proposed model was tested using structural equation modelling.

The results reveal that consumers perceive chefs as human brands and the associated narratives include both performance- and popularity-based characteristics. The findings support the conclusion that individuals give great importance to chefs’ image and the congruence between chefs and their restaurant’s image. In addition, luxury restaurant image only affects novelty experiences, and both hedonic and novelty experiences have a positive effect on customers’ happiness and well-being.

This research focused on Portuguese luxury restaurants. The consumers’ happiness and well-being needs to be replaced by other outcomes to confirm if the model produces consistent results.

The results should help luxury restaurant managers understand more fully which pull factors are valued by their clients and which aspects contribute the most to their pleasure and welfare.

This study adds to the extant literature by exploring consumers’ perceptions of chefs as human brands and the role these chefs’ image play in customers’ luxury restaurant experiences and perceived happiness and well-being.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

The tender documents, an essential data source for internet-based logistics tendering platforms, incorporate massive fine-grained data, ranging from information on tenderee, shipping location and shipping items. Automated information extraction in this area is, however, under-researched, making the extraction process a time- and effort-consuming one. For Chinese logistics tender entities, in particular, existing named entity recognition (NER) solutions are mostly unsuitable as they involve domain-specific terminologies and possess different semantic features.

To tackle this problem, a novel lattice long short-term memory (LSTM) model, combining a variant contextual feature representation and a conditional random field (CRF) layer, is proposed in this paper for identifying valuable entities from logistic tender documents. Instead of traditional word embedding, the proposed model uses the pretrained Bidirectional Encoder Representations from Transformers (BERT) model as input to augment the contextual feature representation. Subsequently, with the Lattice-LSTM model, the information of characters and words is effectively utilized to avoid error segmentation.

The proposed model is then verified by the Chinese logistic tender named entity corpus. Moreover, the results suggest that the proposed model excels in the logistics tender corpus over other mainstream NER models. The proposed model underpins the automatic extraction of logistics tender information, enabling logistic companies to perceive the ever-changing market trends and make far-sighted logistic decisions.

(1) A practical model for logistic tender NER is proposed in the manuscript. By employing and fine-tuning BERT into the downstream task with a small amount of data, the experiment results show that the model has a better performance than other existing models. This is the first study, to the best of the authors' knowledge, to extract named entities from Chinese logistic tender documents. (2) A real logistic tender corpus for practical use is constructed and a program of the model for online-processing real logistic tender documents is developed in this work. The authors believe that the model will facilitate logistic companies in converting unstructured documents to structured data and further perceive the ever-changing market trends to make far-sighted logistic decisions.

This study aims to examine the determinants that influence housing prices in Dammam metropolitan area (DMA), Saudi Arabia, by using the analytic hierarchy process (AHP) model. The study considers determinants such as building age (BLD AG), building size (BLD SZ), building condition (BLD CN), access to parking (ACC PK), proximity to transport infrastructure (PRX TRS), proximity to green areas (PRX GA) and proximity to amenities (PRX AM).

The AHP decision model was used to assess the determinants of housing prices in DMA, using a pair-wise comparison matrix to determine the influence of the investigated factors on housing prices.

The study’s results revealed that building size (BLD SZ) was the most critical determinant affecting housing prices in DMA, with a weight of 0.32, trailed by proximity to transport infrastructure (PRX TRS), with a weight of 0.24 as the second most influential housing price determinant in DMA. The third most important determinant was proximity to amenities (PRX AM), with a weight of 0.18.

This study addresses a research gap by using the AHP model to assess the spatial determinants of housing prices in DMA, Saudi Arabia. Few studies have used this model in examining housing price factors, particularly in the context of Saudi Arabia. Consequently, the findings of this study provide unique insights for policymakers, housing developers and other stakeholders in understanding the importance of building size, proximity to transport infrastructure and proximity to amenities in influencing housing prices in DMA. By considering these determinants, stakeholders can make informed decisions to improve housing quality and prices in the region.