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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.

The human body has the same basic size data but has different surface morphology, resulting in the unfitness even under the same size specification. The purpose of this study was to solve the local fitness problems by representing and quantifying the human surface morphological difference.

Firstly, the 3D point cloud for 323 female students was scanned, and the cross-section layers of the “waist-to-thigh” zone were determined. Secondly, the space vector based on the space Euclidean distance was extracted to represent and quantify the surface morphological difference. And the Principal Component Analysis and K-means were adopted to subdivide the target zone. Thirdly, the pattern based on the subdivision results and surface flattening was generated. Additionally, the fitness was evaluated by the subjective and objective assessments, separately.

The space vector could represent and quantify the shape morphology of the “waist-to-thigh” zone. It had successfully achieved the human body subdivision and corresponding pattern generation for the “waist-to-thigh” zone. And the pattern based on the shape subdivision and surface flattening of the space vector could effectively improve the wearing fitness. Particularly in the waist and crotch area of trousers, the obvious wrinkles had been solved because the space vector is more in line with the shape morphology characteristics.

The proposed method could represent and quantify the difference in human surface morphology in a 3D manner. It solved the unfitness problem caused by the same body size but different shape surface morphology. And it will contribute to the fitness improvement of the trousers.

The diffusion of technologies from other sectors, and innovations in kitchen equipment, fueled structural changes within the foodservice industry. However, this change comes at a price of disrupting the critical step of assessing the demand forecast accuracy. This study aims to explore a surprisingly unique and elevated complexity when assessing the critically important demand forecast accuracy.

The paper develops a mathematical model to describe and explore the nature of the problem in structural biased demand forecast accuracy assessment. It then uses numerical simulation to construct a market example to gain better insights on the bias characteristics. Finally, the forecast accuracy measurement’s inherent bias is contrasted with that of other typical hospitality forecasting setups.

This paper outlines the theoretical underpinnings of how demand forecasts in the central kitchen setup are dynamic and thus produce a structural bias. More specifically, this paper discovers how, in this context of orders from a central location, the forecasts set the capacity constraints, and, consequently, generate a considerably more biased forecast accuracy measure. Relying on such forecast accuracy measures can lead to serious negative business outcomes.

To the best of the author’s knowledge, this study is the first to show that in the unique new technology enabled environment of central kitchen operation, where daily dish demand forecasts set the daily constrained capacity levels, the accuracy measure is severely biased, and consequently accuracy is likely to deteriorate, which in turn, could lead to suboptimal decisions. The major theoretical contribution of this study is a novel analytical model which explains and describes the bias in the accuracy measurement.

技术从其他行业的传播以及厨房设备的创新推动了餐饮业内的结构变化。然而, 这种变化直接影响了评估需求预测准确性。本研究探讨了在餐饮业结构改变后，评估至关重要的需求预测准确性时所面临的令人独特和复杂性。

本文自研了一个数学模型来描述和探讨评估需求预测准确性中的结构性偏差的本质。然后, 使用数值模拟构建一个市场示例, 以更好地了解上述偏差的特征。最后, 将这种预测准确性评估的系统性偏差与其他传统的餐饮业需求预测情境进行对比。

本文概述了中央厨房运营中需求预测是动态的, 因此产生了结构性偏差的理论基础。更具体地说, 在使用中央厨房并集中订单的情境下, 本文发现需求预测直接设定了容量限制, 因此产生了在需求预测准确度衡量中的结构性偏差。依赖这样的预测准确性度量可能产生严重的负面商业结果。

这项研究首次表明, 在中央厨房运营的独特的新环境中, 由于新的设定即每日菜品需求预测直接决定每日容量水平, 需求预测准确度衡量标准有着严重偏差, 长期来讲准确性可能下降, 从而导致次优的商业决策。本研究的主要理论贡献是提供一个餐饮企业在新运营环境中解释和描述需求预测准确度中结构性偏差的全新分析模型。

The purpose of this paper is to understand what inhibit or facilitate cloud computing (CC) assimilation.

The authors investigate the effects of two enablers, top management support (TMS) and government support (GS), and two inhibitors, organization inertia (OI) and data security risk (DSR) on CC assimilation. The authors posit that enablers and inhibitors influence CC assimilation separately and interactively. The research model is empirically tested by using the field survey data from 376 Chinese firms.

Both TMS and GS positively and DSR negatively influence CC assimilation. OI negatively moderates the TMS–assimilation link, and DSR negatively moderates the GS–assimilation link.

The results indicate that enablers and inhibitors influence CC assimilation in both separate and joint manners, suggesting that CC assimilation is a much more complex process and demands new knowledge to be learned.

For these firms with a high level of OI, only TMS is not enough, and top managers should find other effective way to successfully implement structural and behavioral change in the process of CC assimilation. For policy makers, they should actively play their supportive roles in CC assimilation.

A new framework is developed to identify key drivers of CC assimilation along two bipolar dimensions including enabling vs inhibiting and internal vs external.

Stiffened plates have been widely used in civil, marine, aerospace engineering. As a kind of thin-walled structure operating in complex environment, stiffened plates mostly undergo a variety of dynamic loads, which may sometimes result in large-amplitude vibration. Additionally, initial stresses and geometric imperfections are widespread in this type of structure. Furthermore, it is universally known that initial stresses and geometric imperfections may affect mechanical behavior of structures severely, particularly in dynamic analysis. Thus, the purpose of this paper is to study the stress variation rule of a stiffened plate during large-amplitude vibration considering initial stresses and geometric imperfections.

The initial stresses are represented in the form of initial bending moments applying to the stiffened plate, while the initial geometric imperfections are considered by means of trigonometric series, and they are assumed existing in the plate along the z-direction exclusively. Then, the dynamic equilibrium equations of the stiffened plate are established using Lagrange’s equation as well as aforementioned conditions. The nonlinear differential equations of motion are simplified as a two-degree-of-freedom system by considering 1:2 and 1:3 internal resonances, respectively, and the multiscale method is applied to solve the equations.

The influence of initial stresses on the plate, stresses during internal resonance is remarkable, while that is moderate for initial geometric imperfections. (Upon considering the existence of initial stresses or geometric imperfections, the stresses of motivated modes are less than the primary mode for both and internal resonances). The influence of bidirectional initial stresses on the plate’s stresses during internal resonance is more remarkable than that of unidirectional initial stresses. The coupled vibration in 1%3A2 internal resonance is fiercer than that in internal resonance.

Stiffened plates are widely used in engineering structures. However, as a type of thin-walled structure, stiffened plates vibrate with large amplitude in most cases owning to their complicated operation circumstance. In addition, stiffened plates usually contain initial stresses and geometric imperfections, which may result in the variation of their mechanical behavior, especially dynamical behavior. Based on the above consideration, this paper studies the nonlinear dynamical behavior of stiffened plates with initial stresses and geometrical imperfections under different internal resonances, which is the originality of this work. Furthermore, the research findings can provide references for engineering design and application.

Mean absolute percentage error (MAPE) is the primary forecast evaluation metric in hospitality and tourism research; however its main shortcoming is that it is asymmetric. The asymmetry occurs due to over or under forecasts that introduce bias into forecast evaluation. This study aims to explore the nature of asymmetry and designs a new measure, one that reduces the asymmetric properties while maintaining MAPE’s scale-free and intuitive interpretation characteristics.

The study proposes and tests a new forecasting accuracy measure for hospitality revenue management (RM). A computer simulation is used to assess and demonstrate the problem of asymmetry when forecasting with MAPE, and the new measures’ (MSapeMER, that is, Mean of Selectively applied Absolute Percentage Error or Magnitude of Error Relative to the estimate) ability to reduce it. The MSapeMER’s effectiveness is empirically validated by using a large set of hotel forecasts.

The study demonstrates the ability of the MSapeMER to reduce the asymmetry bias generated by MAPE. Furthermore, this study demonstrates that MSapeMER is more effective than previous attempts to correct for asymmetry bias. The results show via simulation and empirical investigation that the error metric is more stable and less swayed by the presence of over and under forecasts.

It is recommended that hospitality RM researchers and professionals adopt MSapeMER when using MAPE to evaluate forecasting performance. The MSapeMER removes the potential bias that MAPE invites due to its calculation and presence of over and under forecasts. Therefore, forecasting evaluations may be less affected by the presence of over and under forecasts and their ability to bias forecasting results.

Hospitality RM should adopt this measure when MAPE is used, to reduce biased decisions driven by the “asymmetry of MAPE.”

The MAPE error metric exhibits an asymmetry problem, and this paper proposes a more effective solution to reduce biased results with two major methodological contributions. It is first to systematically study the characteristics of MAPE’s asymmetry, while proposing and testing a measure that considerably reduces the amount of asymmetry. This is a critical contribution because MAPE is the primary forecasting metric in hospitality and tourism studies. The second methodological contribution is a procedure developed to “quantify” the asymmetry. The approach is demonstrated and allows future research to compare asymmetric characteristics among various accuracy measures.

This paper aims to investigate the temporal dynamics of users browsing activity on a hotel website in order to derive effective marketing strategies and constantly improve website effectiveness. Users' activities on the hotel's website on yearly, monthly, daily and hourly basis are examined and compared, demonstrating the power of informatics and data analytics.

A total of 29,976 hourly Weblog files from 1 August 2014 to 31 December 2017 were collected from a luxury hotel in Hong Kong. ANOVA and post-hoc comparisons were used to analyse the data.

Users' browsing behaviours, particularly stickiness, on the hotel website differ on yearly, monthly, daily and weekly bases. Users' activities increased steadily from 2014 to 2016, but dropped in 2017. Users are most active from July to September, on weekdays, and from noon to evening time. The month-, day-, and hour-based behaviours changed through years. The analysis of big data determines strategic and operational management and marketing decision-making.

Understanding the usage patterns of their websites allow organisations to make a range of strategic, marketing, pricing and distribution decisions to optimise their performance. Fluctuation of website usage and level of customer engagement have implications on customer support and services, as well as strategic partnership decisions.

Leveraging the power of big data analytics, this paper adds to the existing literature by performing a comprehensive analysis on the temporal dynamics of users' online browsing behaviours.

The purpose of this paper is to examine whether the institutions play a role in tourism development and international recognition, specifically the influence of marketization on the international tourists’ inbound arrivals in different Chinese provinces.

This paper constructs a demand model of tourism and empirically analyzes the relationship between marketization and inbound tourism demand with the panel data of the provinces of China and NERI Index of Marketization.

Marketization does have an influence on inbound tourism demand of China. Specially, the relationship between government and market, the development of product market, the market intermediary organizations and the legal system environment can increase the demand of the foreign tourists to visit China, although the magnitudes are different.

This paper argues that the qualities of marketization intuitions are important in increasing inbound tourism, given that it can bring better tourism experience and improve the international recognition. Strengthening the legislation and protecting the legitimate rights and interests of consumers can attract more international travelers to China. Market distribution of competitive economic resources, reducing political intervention into corporate activities and relieving tax burdens of enterprises can improve the competitiveness and the service qualities of Chinese domestic tourism firms.

This paper leads the discussions of institutions and tourism. It combines the consumer theory and uses static and dynamic panel data models to analyze the influencing factors of Chinese tourism. It argues that Chinese inbound tourism shall develop with the systemic marketization progress in China.

The purpose of this paper is to compare the relative impacts of trust and risk on individual’s transaction intention in consumer-to-consumer (C2C) e-marketplaces from both the buyers’ and the sellers’ perspectives.

Two surveys were used to collect data regarding buyers’ and sellers’ perceptions and transaction intentions at a typical C2C e-marketplace. Partial least squares was used to analyze the data. A complementary qualitative study was conducted to triangulate the results from the quantitative study.

Institution-based trust (IBT) exerts a stronger influence on transaction intentions for buyers than for sellers. Sellers perceive a stronger impact of trust in intermediary (TII) than buyers on transaction intentions. The impacts of perceived risk in transactions are not different between buyers and sellers. Furthermore, IBT mediates the impacts of TII and perceived risk on transaction intentions for buyers.

The results indicate that the impacts of trust and risk on transaction intention in e-marketplaces do differ between buyers and sellers. This suggests a need to further investigate the buyer–seller difference in online transactions.

Intermediaries need to focus on different types of trust-building mechanisms when attracting buyers and sellers to make transactions in the e-marketplace.

C2C e-marketplaces cannot survive without participation from both buyers and sellers. Most prior research is conducted from the buyers’ perspective. This research sets a starting point for future research to further explore the differences between buyers’ and sellers’ behavior in C2C e-commerce environments.

The focus of industrial cluster innovation lies in the cooperation between enterprises and universities/scientific research institutes to make a theoretical breakthrough in the system and mechanism of industrial cluster network. Under the theoretical framework of cluster network, industrial structure can be optimized and upgraded, and enterprise benefit can be improved. Facing the increasing proliferation and multi-structured enterprise data, how to obtain potential and high-quality innovation features will determine the ability of industrial cluster network innovation, as well as the paper aims to discuss these issues.

Based on complex network theory and machine learning method, this paper constructs the structure of “three-layer coupling network” (TLCN), predicts the innovation features of industrial clusters and focuses on the theoretical basis of industrial cluster network innovation model. This paper comprehensively uses intelligent information processing technologies such as network parameters and neural network to predict and analyze the industrial cluster data.

From the analysis of the experimental results, the authors obtain five innovative features (policy strength, cooperation, research and development investment, centrality and geographical position) that help to improve the ability of industrial clusters, and give corresponding optimization strategy suggestions according to the result analysis.

Building a TLCN structure of industrial clusters. Exploring the innovation features of industrial clusters. Establishing the analysis paradigm of machine learning method to predict the innovation features of industrial clusters.