Search results

In recent years, electronic word-of-mouth (e-WOM) concerning travel products reflected in online review information has become an important reference for tourists to make their product purchase decisions, while for travel service providers (TSPs), monitoring and improving the e-WOM of their travel products is always an important task. Therefore, based on the online review information, how to capture e-WOM of travel products and find out specific ways to improve the e-WOM is a noteworthy research problem. The purpose of this paper is to develop a method for capturing and analyzing e-WOM toward travel products based on sentiment analysis and stochastic dominance.

Specifically, online review information of travel products is first crawled and preprocessed. Second, sentiment strengths of online review information toward travel products concerning each feature are judged. Then, the matrix of structured online review information toward travel products is formed. Further, the matrix of e-WOM comparisons between any two travel products is constructed, and e-WOM ranking concerning each travel product is determined. Finally, trade-off chart models are constructed to conduct the e-WOM improvement analyses concerning the travel products.

An empirical study based on the online review information toward six travel products crawled from the Tuniu.com website is given to illustrate the use of the proposed method.

The proposed method can not only realize the real-time e-WOM monitoring to travel products but also be useful for TSPs to improve the e-WOM of their travel products.

The paper aims to accurately measure the key motion parameters, such as velocity, azimuth and pitch angle, of the small flying object with a non-uniform curve trajectory. It proposes a measurement method and its calculation model of non-uniform curve trajectory using a photoelectric sensor array.

First, the basic composition of the measurement system and mechanism of photoelectric sensor array are described, respectively. Second, a non-uniform curve mathematical measurement model is constructed differently from the traditional linear trajectory, taking into account the influence of gravity and air resistance. Third, the measurement error of the system is analyzed through numerical simulation. Finally, the accuracy and feasibility of the approach are verified by live-ammunition experiments.

The results show that the systematic error of the hitting point coordinates can be reduced by 9% compared to the traditional linear measurement model. Consequently, this method can meet the higher measurement requirement for the key motion parameters of the small flying object under the non-uniform curve trajectory. Research limitations/implications (if applicable)- although the approach itself is generalizable, the method is unable to detect the motion parameters of multiple small flying objects.

Although the approach itself is generalizable, the method is unable to detect the motion parameters of the multiple small flying objects.

It is evident that the proposed non-uniform curve measurement model is more precise in quantifying the essential characteristics of the small flying object, particularly in consideration of the environmental conditions.

The precise measurement of the key motion parameters of the small flying object can facilitate the enhancement of the protective performance of protective materials.

A novel approach to measurement is proposed, which differs from the conventional uniform trajectory model. To this end, the space construction of the photoelectric sensor array is optimized. The number of the sensors is revised.

A new reverse educing method (REM) is presented to estimate the vibrational stress on rotating aeroengine blades from the actual fracture section. This method does not conform to the traditional calculating method, but involves the reverse solution idea. The value of the crack propagation ratio da/dN can be obtained from the actual fracture section of the blade, and then the vibrational stress on rotating blades can be calculated through static computation, modal analysis and crack stress intensity factor calculation. The result is very significant for fault diagnosing and shooting of aeroengine blades.

The purpose of this paper is to present a novel framework for recommending desirable products to active customers with the consideration of not only their preferences but also the products’ quality performances and their e-retailers’ service performances under e-commerce.

A framework in support of the product recommendation is presented. Three modules are involved in the framework, i.e. data collection and preference analysis module, hybrid recommendation module and recommendation generation module. First, preferences of different types of customers are inferred through analysis of their behavioral data and then a paradigm is adopted based on cased-based reasoning to generate candidate recommendation products. Further, recommendation lists for different types of customers are obtained through measurement of the quality and service performances concerning each candidate recommendation product.

To illustrate the performance of the presented framework, a simulation study comparing the approach developed based on the framework and the traditional approach is conducted. The experiment results show that the developed approach outperformed the traditional approach in term of average rank score. This means that incorporating the consideration of product performance and customer service factors can play an important role in product recommendations.

The presented framework can overcome the defect that low conversion rate of recommended products to actually purchased ones suffered by the traditional approach. In addition, the use of the presented framework can not only help customers to obtain desirable products and save searching time but also supervise and urge e-retailers to pay more attention to the quality and service performances.

The purpose of this paper is to explore the flow and heat transfer characteristics of the jet impingement onto a conical heat sink and evaluate the ability of heat transfer enhancement.

A numerical study of the flow and heat transfer of liquid impingement on cone heat sinks was conducted, and transition SST turbulence model was validated and adopted. The flow and thermal performances were investigated with the Reynolds number that ranges from 5,000 to 23,000 and cone angle that ranges from 0° to 70° in four regions.

Local Nusselt numbers are large, and pressure coefficients drop rapidly near the stagnation point. In the conical bottom edge, a secondary inclined jet was observed, thereby introducing a horseshoe vortex that causes drastic fluctuations in the curves of the flow and heat transfer. The average Nusselt numbers are higher in a conical protuberance than in flat plates in most cases, thus indicating that the heat transfer performance of jet impingement can be improved by a cone heat sink. The maximum increase is 13.6 per cent when the cone angle is 60°, and the Reynolds number is 23,000.

The flow and heat transfer behavior at the bottom edge of the cone heat sink is supplemented. The average heat transfer capacity of different heat transfer radii was evaluated, which provided a basis for the study of cone arrays.

This paper aims to present ball bearings with a composite structure based on the bionics principle and shows the comparison between five types of different structures.

By means of the finite element method, the stress and other parameters between different structures are compared and verified. Finally, the comprehensive parameters of different structures are evaluated by the analytic hierarchy process method.

The evaluation of the comprehensive parameters of five types of structures is shown here.

The value of this paper is calculated and compared to the parameters of five types of different structures, and the parameter score evaluation of each structure is given. Different structures can be selected according to different parameter requirements, which to provide a theoretical basis for the design of ball bearings.

The peer review history for this article is available at: https://publons.com/publon10.1108/ILT-10-2019-0413

Prediction of excess pore water pressure and estimation of soil parameters are the two key interests for consolidation problems, which can be mathematically quantified by a set of partial differential equations (PDEs). Generally, there are challenges in solving these two issues using traditional numerical algorithms, while the conventional data-driven methods require massive data sets for training and exhibit negative generalization potential. This paper aims to employ the physics-informed neural networks (PINNs) for solving both the forward and inverse problems.

A typical consolidation problem with continuous drainage boundary conditions is firstly considered. The PINNs, analytical, and finite difference method (FDM) solutions are compared for the forward problem, and the estimation of the interface parameters involved in the problem is discussed for the inverse problem. Furthermore, the authors also explore the effects of hyperparameters and noisy data on the performance of forward and inverse problems, respectively. Finally, the PINNs method is applied to the more complex consolidation problems.

The overall results indicate the excellent performance of the PINNs method in solving consolidation problems with various drainage conditions. The PINNs can provide new ideas with a broad application prospect to solve PDEs in the field of geotechnical engineering, and also exhibit a certain degree of noise resistance for estimating the soil parameters.

This study presents the potential application of PINNs for the consolidation of soils. Such a machine learning algorithm helps to obtain remarkably accurate solutions and reliable parameter estimations with fewer and average-quality data, which is beneficial in engineering practice.

This paper aims to offer an extensive empirical case study analysis by investigating housing affordability in Turkey as a whole, and in Istanbul, Ankara and Izmir over the period of 2006 and 2017 and its sub-periods.

This paper develops a theoretically informed model to assess affordability using complementary methodologies in quantitative analysis. This study seeks to help outline the nature of the problem in aggregate level and in the cities; it also seeks to offer lessons about how to address measurement and modelling challenges in emergent market contexts by constructing aggregate-/city-level housing cost-to-income (HCI) ratio, adjusted HCI (AHCI) ratio, housing affordability index (HAI) and effective HAI sensitive to multiple calculation methodologies and alternative data set involving income distribution and poverty tranches.

HCI, AHCI, HAI and EHAI models generally suggest the parallel results: housing is not affordable in Turkey and in Istanbul, Ankara and Izmir except for the highest income groups. The evidence implies that besides macroeconomic instabilities, distorted interest rates and short average mortgage maturity, poverty and unequal income/wealth distributions are the main reasons of the Turkish housing affordability crisis specifically heightened in metropolitan areas such as in Istanbul.

The evidence provides an insight on housing affordability problems in Turkey. However, small sample size and short observation period create a limit for generalisation of the findings. Further analysis would be required to illustrate how housing affordability changes in different cities of Turkey in a longer period.

By using empirical approaches, this paper helps to understand how serious housing affordability problems of Turkey in aggregate and urban levels. This evidence helps to explain declining ownership ratio in low-income groups and in urban areas. Reliable explanations on existing housing crisis of Turkey also help to develop affordable housing policies.

Declining housing affordability and homeownership ratio may translate as the rising housing inequality and insecurity among Turkish households. Moreover, better affordability values of higher income groups suggest that existing inequality, economic/social segmentation, and hence social tension between high and low income groups, may further increase. In this respect, the authors suggest socially important policies such as reducing income/wealth inequalities and increasing affordable housing supply.

This study offers a detailed empirical case study analysis that can be used as an exemplar of how to overcome data constraints in other evolving housing market contexts. This study sets out an approach overcoming the challenges of measurement. This study also combines existing methodological approaches with the modified variables to provide a more realistic aggregate-/urban-level housing affordability picture. The authors calculated some parts of housing affordability ratio and index series using discretionary income, minimum wage and effective minimum wage to show the variations of different measurement approaches. Some constructed series are also sensitive to income distribution and poverty thresholds. Collectively, this empirical approach, developed by using emerging market data, provides a contribution to the literature.

Cybersecurity attacks on critical infrastructures, businesses and nations are rising and have reached the interest of mainstream media and the public’s consciousness. Despite this increased awareness, humans are still considered the weakest link in the defense against an unknown attacker. Whatever the reason, naïve-, unintentional- or intentional behavior of a member of an organization, the result of an incident can have a considerable impact. A security policy with guidelines for best practices and rules should guide the behavior of the organization’s members. However, this is often not the case. This paper aims to provide answers to how cybersecurity-related behavior is assessed.

Research questions were formulated, and a systematic literature review (SLR) was performed by following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. The SLR initially identified 2,153 articles, and the paper reviews and reports on 26 articles.

The assessment of cybersecurity-related behavior can be classified into three components, namely, data collection, measurement scale and analysis. The findings show that subjective measurements from self-assessment questionnaires are the most frequently used method. Measurement scales are often composed based on existing literature and adapted by the researchers. Partial least square analysis is the most frequently used analysis technique. Even though useful insight and noteworthy findings regarding possible differences between manager and employee behavior have appeared in some publications, conclusive answers to whether such differences exist cannot be drawn.

Research gaps have been identified, that indicate areas of interest for future work. These include the development and employment of methods for reducing subjectivity in the assessment of cybersecurity-related behavior.

To the best of the authors’ knowledge, this is the first SLR on how cybersecurity-related behavior can be assessed. The SLR analyzes relevant publications and identifies current practices as well as their shortcomings, and outlines gaps that future research may bridge.

The purpose of this study is to identify the determinants of success in peer-to-peer (P2P) lending campaigns, especially amid global financial disruptions like the COVID-19 pandemic. Addressing a notable gap in current research, we explore how factors such as firm uncertainty, loan characteristics (interest rates and maturity) and venture quality (human, social and intellectual capital) influence P2P lending effectiveness. Using multiple regression analysis on data from 523 projects on the October platform, our study aims to enhance the understanding and operational efficiency of P2P platforms, contributing to a more resilient financial ecosystem.

This study employs a quantitative research design using multiple regression analysis to examine the impact of specific variables on the success of P2P lending campaigns. Data were collected from 523 concluded P2P lending projects on the October platform, spanning from 2015 to 2021. Variables of interest include the level of uncertainty of the firm, loan characteristics such as interest rate and maturity and the quality of the venture assessed through human, social and intellectual capital. This method allows for a robust analysis of the factors contributing to the success of P2P lending within a dynamic financial context.

The findings of this study reveal that the success of P2P lending campaigns is significantly influenced by the level of uncertainty of the firm, the interest rate of the loan and the quality of the venture. Specifically, higher uncertainty in firms correlates negatively with campaign success, while competitive interest rates positively impact funding outcomes. Furthermore, ventures that demonstrate robust human capital, particularly those with management teams that possess diverse skills and high qualifications, tend to attract more funding. These results underscore the critical role of strategic financial and human resource planning in enhancing the effectiveness of P2P lending platforms.

This study contributes uniquely to the literature by integrating multiple variables – firm uncertainty, loan characteristics and venture quality – into a comprehensive analysis of success factors in P2P lending. It addresses the scarcity of research examining the combined effects of these factors, particularly in the context of global financial disruptions like the COVID-19 pandemic. By focusing on a specific European platform during a dynamic period, this research provides new insights into how P2P lending can adapt to and thrive amid financial crises. The findings offer valuable guidance for both practitioners and policymakers aiming to optimize P2P lending practices in uncertain economic landscapes.