Search results

As a distinctive form of communist heritage tourism, the ideology and government-led form of red tourism warrants an in-depth examination of how tourists consume and perceive it. This study aims to reveal tourists’ perception of red tourism through the lens of destination image.

This study collected 9,819 user-generated photographs within four types of red tourism destinations (RTDs) and used a computer visual and semiotic analysis approach to conduct photograph-based cognitive and affective attributes extraction. Network analysis further visualized the co-relations between cognitive images and affective images. ANOVA analysis compared the differences of the four types of destination images.

Ten dimensions of cognitive image and eight categories of affective image of red tourism were identified. It found that monuments, statues, memorial symbols were the distinctive cognitive features, and admiration was the most dominant emotion. Heterogeneity of destination images was identified among the four types of RTDs.

To the best of the authors’ knowledge, the study is one of the first to explore tourists’ consumption of red tourism through the lens of destination image, which reveals the inconsistencies between the officially projected images and tourists’ perceived images of red tourism. Using Plutchik’s model, it validates a series of positive and negative emotions contributing to the affective images of red tourism, which expands the findings of emotions within the extant red tourism research. Through combined applications of computer visual and semiotic analysis, ANOVA, network analysis and model visualization, the study provides an important methodological triangulation for photograph-based destination image studies.

红色旅游作为共产主义旅游的独特形式, 游客如何感知这种国家意识形态植入与政府主导型旅游值得深入研究。本研究旨在从目的地意象视角揭示游客红色旅游感知。

本研究收集四种类型的红色旅游地9819张用户生成照片, 利用计算机视觉-情感析法对照片进行认知和情感元素提取。复杂网络分析揭示了认知意象与情感意象之间的关联。方差分析比较了四种红色旅游地意象的差异。

本研究确定了红色旅游认知意象的十个维度和情感意象的八个类别。研究发现, 纪念碑、雕像、纪念符号是其独特的认知意象元素, 钦佩是其最主要的情感，四种类型红色旅游地意象存在差异性。

本文是同类研究中首次从目的地意象视角探索游客对红色旅游地感知, 揭示了红色旅游官方投射意象与游客感知意象之间的差异。利用Plutchik情感之轮模型, 验证了一系列积极和消极情绪构成红色旅游地情感意象, 拓展了红色旅游的情感发现。综合运用计算机视觉-情感分析、方差分析、网络分析和模型可视化等方法, 为基于照片的旅游目的地意象研究提供了一个重要方法。

Como forma distintiva del turismo del patrimonio comunista, la ideología y la forma gubernamental del turismo rojo justifican un examen en profundidad de cómo lo consumen y perciben los turistas. Este estudio pretende revelar la percepción que tienen los turistas del turismo rojo desde la perspectiva de la imagen del destino.

Este estudio recopiló 9.819 fotografías generadas por los usuarios dentro de cuatro tipos de destinos de turismo rojo, y utilizó un enfoque de análisis visual y semiótico por ordenador para llevar a cabo la extracción de atributos cognitivos y afectivos basados en fotografías. El análisis de redes visualizó además las correlaciones entre las imágenes cognitivas y las imágenes afectivas. El análisis ANOVA comparó las diferencias de los cuatro tipos de imágenes de destino.

Se identificaron diez dimensiones de imagen cognitiva y ocho categorías de imagen afectiva del turismo rojo. Se descubrió que los monumentos, las estatuas y los símbolos conmemorativos eran los rasgos cognitivos distintivos, y la admiración la emoción más dominante. Se identificó una heterogeneidad de imágenes de destino entre los cuatro tipos de destinos de turismo rojo.

El estudio es uno de los primeros en explorar el consumo de turismo rojo por parte de los turistas a través de la lente de la imagen del destino, lo que revela las incoherencias entre las imágenes proyectadas oficialmente y las imágenes percibidas por los turistas del turismo rojo. Utilizando el modelo de Plutchik, valida una serie de emociones positivas y negativas que contribuyen a las imágenes afectivas del turismo rojo, lo que amplía los hallazgos sobre las emociones dentro de la investigación existente sobre el turismo rojo. Mediante aplicaciones combinadas de análisis visual y semiótico por ordenador, ANOVA, análisis de redes y visualización de modelos, el estudio proporciona una importante triangulación metodológica para los estudios de la imagen del destino basados en fotografías.

The purpose of the study is to identify the soft skills and abilities that are crucial to success in the fields of operations management (OM) and supply chain management (SCM), using the O*NET database and the classification of a set of professional figures integrating values for task skills and abilities needed to operate successfully in these professions.

The study used the O*NET database to identify the soft skills and abilities required for success in OM and SCM industries. Correlation analysis was conducted to determine the tasks required for the job roles and their characteristics in terms of abilities and soft skills. ANOVA analysis was used to validate the findings. The study aims to help companies define specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the job position.

As a result of the work, a set of soft skills and abilities was defined that allow, through correlation analysis, to explain a large number of activities required to work in the operations and SCM (OSCM) environment.

The work is inherently affected by the database used for the professional figures mapped and the scores that are attributed within O*NET to the analyzed elements.

The information resulting from this study can help companies develop specific assessments and tests for the roles of OM and SCM to measure individual attitudes and correlate them with the requirements of the job position. The study aims to address the need to identify soft skills in the human sphere and determine which of them have the most significant impact on the OM and SCM professions.

The originality of this study lies in its approach to identify the set of soft skills and abilities that determine success in the OM and SCM industries. The study used the O*NET database to correlate the tasks required for specific job roles with their corresponding soft skills and abilities. Furthermore, the study used ANOVA analysis to validate the findings in other sectors mapped by the same database. The identified soft skills and abilities can help companies develop specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the requirements of the job position. In addressing the necessity for enhanced clarity in the domain of human factor, this study contributes to identifying key success factors. Subsequent research can further investigate their practical application within companies to formulate targeted growth strategies and make appropriate resource selections for vacant positions.

This study illustrates the economic and financial dynamics of the sector, analysing the evolution of the main ratios of profitability and financial structure of 1,559 Italian real estate companies divided into the three macro-regions: North, Centre and South, in the period 2011–2020. In this way, it is also possible to verify the responsiveness to the 2020 pandemic crisis.

The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance, supplemented by the Tukey–Kramer test, to identify significant differences between the three Italian macro-regions.

The study shows the increase in profitability after the 2008 crisis, despite its reverberation in the years 2012–2013. The financial structure of companies improved almost everywhere. The pandemic had modest effects on performance.

In the future, other indices should be considered to gain a more comprehensive view. This is a quantitative study based on financial statements data that neglects other important economic and social factors.

Public policies could use this study for better interventions to support the sector. In addition, internal management can compare their company's performance with the industry average to identify possible improvements.

The research analyses an economic field that employs a large number of people, especially when considering the construction and real estate services covered by this analysis.

The study contributes to the literature by providing a quantitative analysis of industry dynamics, with comparative information that can be deduced from financial statements over the years.

The purpose of this study is to investigate the impact of titanium oxide (TiO₂) filler on the abrasive wear properties of bamboo fiber reinforced epoxy composites (BFRCs) using a Taguchi approach. The study aims to enhance the abrasive wear resistance of these composites by introducing TiO₂ filler as a potential reinforcement, thus contributing to the development of sustainable and environmentally friendly materials.

This study focuses on the fabrication of epoxy/bamboo composites infused with TiO₂ particles within the Wt.% range of 0–8 Wt.% using hand layup techniques. The resulting composites were subjected to wear testing according to ASTM G99-05 standards. Statistical analysis of the wear results was carried out using the Taguchi design of experiments (DOE). Additionally, an analysis of variance (ANOVA) was used to determine the influential control factors impacting the specific wear rate (SWR) and coefficient of friction (COF).

The study illuminates how integrating TiO₂ filler enhances abrasive wear in epoxy/bamboo composites. Statistical analysis of SWR highlights abrasive grit size (grit) as the most influential factor, followed by normal load, Wt.% of TiO₂ and sliding distance. Analysis of the COF identifies normal load as the primary influential factor, followed by grit, Wt.% of TiO₂ and sliding distance. The Taguchi predictive model closely aligns with experimental results, validating its reliability. The morphological study revealed significant differences between the unfilled and TiO₂-filled composites. The inclusion of TiO₂ improved wear resistance, as evidenced by reduced surface damage and wear debris.

This research paper aims to integrate TiO₂ filler and bamboo fibers to create an innovative hybrid composite material. TiO₂ micro and nanoparticles show promise as filler materials, contributing to improved tribological properties of epoxy composites. The utilization of Taguchi’s DOE and ANOVA for statistical analysis provides valuable guidance for academic researchers and practitioners in optimizing control variables, especially in the context of natural fiber reinforced composites.

Social engineering is crucial in today’s digital landscape. As technology advances, malicious individuals exploit human judgment and trust. This study explores how age, education and occupation affect individuals’ awareness, skills and perceptions of social engineering.

A quantitative research approach was used to survey a diverse demographic of Egyptian society. The survey was conducted in February 2023, and the participants were sourced from various Egyptian social media pages covering different topics. The collected data was analyzed using descriptive and inferential statistics, including independent samples t-test and ANOVA, to compare awareness and skills across different groups.

The study revealed that younger individuals and those with higher education tend to research social engineering more frequently. Males display a higher level of awareness but score lower in terms of social and psychological consequences as well as types of attacks when compared to females. The type of attack cannot be predicted based on age. Higher education is linked to greater awareness and ability to defend against attacks. Different occupations have varying levels of awareness, skills, and psychosocial consequences. The study emphasizes the importance of increasing awareness, education and implementing cybersecurity measures.

This study’s originality lies in its focus on diverse Egyptian demographics, innovative recruitment via social media, comprehensive exploration of variables, statistical rigor, practical insights for cybersecurity education and diversity in educational and occupational backgrounds.

One of the most well-known, seminal models in the tourism field is the one proposed 50 years ago by Stanley C. Plog. His venturesomeness model has been widely cited in journal articles, textbooks, and has also been used as a reference for planning and designing tourism marketing projects. However, empirical research using Plog's psychographics has yielded varied results, some of which have corroborated his model, while others have found partial or no support for some postulates. Therefore, the purpose of this chapter is to offer an exhaustive review of 47 studies in the literature which have employed Plog's venturesomeness concept to examine travelers' personality traits, attitudes, and behavior, synthetizing empirical findings and drawing conclusions from the cumulative results. A discussion of the model's contribution to the current body of knowledge, managerial implications for tourism practitioners, and directions for future research are presented.

Public–private partnership (PPP) projects play a pivotal role in fostering infrastructure development in developing countries. This study aims to identify major barriers and critical success factors (CSFs) for PPP projects in Vietnam.

The study initially conducted an extensive literature review and interviewed industry experts to compile a comprehensive list of barriers and success factors. Subsequently, a survey involving 250 construction professionals was conducted to assess these identified factors. The research used both ANOVA test and hierarchical regression analysis to explore the relationship between participants’ characteristics and assessments.

The results could provide a valuable reference for practitioners by assessing barriers and CSFs in PPP projects. Specifically, the ANOVA analysis indicated a positive correlation between barrier assessment and participants’ experience, while the influence of participants’ sector on the evaluation was minor. Furthermore, the hierarchical regression analysis demonstrated that participants’ sector did not moderate the relationship between barrier assessment and industry experience.

This research contributes to the existing literature by offering empirical insights into barriers and CSFs specific to PPP projects in a developing country context. The findings highlight the crucial role of experienced professionals in ensuring PPP project success. Moreover, the study provides construction practitioners with a better understanding of the essential factors and supports the development of effective management strategies for future PPP projects.

The purpose of this study is to investigate and evaluate the impact of varying proportions of reinforcement on the mechanical properties of a modified Al₂O₃-LM6 cast composite under self-pouring temperature conditions. This study aims to determine the optimal mixture proportion of fine powders of Al, Si and xAl₂O₃ (with x values of 2%, 3% and 4%) through the application of design of experiment (DoE) and statistical analysis using the Minitab software. This study also involved evaluating the microstructural estimation and other physical properties of the cast composite to understand the combined effect of the reinforcement proportion on the material’s properties.

The researchers initially mixed the powders through ball milling and then compacted the moisture-free powder mix in a closed steel die. The resulting preforms were heated at the self-pouring temperature in an inert environment to fabricate the final cast composite. By applying DoE and performing an analysis of variance (ANOVA), the researchers sought to optimize the mixture proportion that would yield the best mechanical properties.

The experimental results indicated that a mixture combination of 83.5% Al blended with 12.5% Si and 4% Al₂O₃ led to the greatest improvement in mechanical properties, specifically in terms of increased density, hardness and impact strength. The ANOVA further supported the interaction effect of each processing parameter on the observed results. The results of this study offer valuable insights for the fabrication of modified Al₂O₃-LM6 cast composites under self-pouring temperature conditions. The identified optimal mixture proportion provides guidance for manufacturing processes and material selection to achieve improved mechanical properties in similar applications.

This study focuses on a specific composite material consisting of modified Al₂O₃ and LM6. Although Al₂O₃ and LM6 have been studied individually in various contexts, the combination of these materials and their impact on mechanical properties under self-pouring temperature conditions is a novel aspect of this research. The researchers use DoE methodology, along with statistical analysis using Minitab software, to optimize the mixture proportion and analyze the data. This systematic approach allows for a comprehensive exploration of the parameter space and the identification of significant factors that influence the mechanical properties of the composite.

This study aims to investigate the optimal process parameters of the wire-cut electrical discharge machining (WCEDM) for the machining of the GZR-AA7475 hybrid metal matrix composite (HMMC). HMMCs are prepared with 2 Wt.% graphite and 4 Wt.% zirconium dioxide reinforced with aluminium alloy 7475 (GZR-AA7475) composite by using the stir casting method. The objective is to enhance the mechanical properties of the material while preserving its unique features. WCEDM with a 0.18 mm molybdenum wire electrode is used for machining the composite.

To conduct experimental studies, a Taguchi L27 orthogonal array was adopted. Input variables such as peak current (I_p), pulse-on-time (T_ON) and flushing pressure (P_F) were used. The effect of process parameters on the output responses, such as material removal rate (MRR), surface roughness rate (SRR) and wire wear ratio (WWR), were investigated. The grey relational analysis (GRA) is used to obtain the optimal combination of the process parameters. Analysis of variance (ANOVA) was also used to identify the significant process parameters affecting the output responses.

Results from the current study concluded that the optimal condition for grey relational grade is obtained at T_ON = 105 µs, I_p = 100 A and P_F = 90 kg/cm². Peak current is the most prominent parameter influencing the MRR, whereas SRR and WRR are highly influenced by flushing pressure.

Identifying the optimal process parameters in WCEDM for machining of GZR-AA7475 HMMC. ANOVA and GRA are used to obtain the optimal combination of the process parameters.

This study aims to identify the significant factors of the multi-dimpling process, determine the most influential parameters of multi-dimpling to increase the dimple sheet strength and make a low-cost model of the multi-dimpling for sheet metal industries. To create an empirical expression linking process performance to different input factors, the percentage contribution of these elements is also calculated.

Taguchi grey relational analysis is used to apply a new effective strategy to experimental data in order to optimize the dimpling process parameters while taking into account several performance factors and low-cost model. In addition, a statistical method called ANOVA is used to ensure that the results are adequate. The optimal process parameters that generate improved mechanical properties are determined via grey relational analysis (GRA). Every level of the process variables, a response table and a grey relational grade (GRG) has been established.

The factors created for experiment number 2 with 0.5 mm as the sheet thickness, 2 mm dimple diameter, 0.5 mm dimple depth, 8 mm dimples spacing and the material of SS 304 were allotted rank one, which belonged to the optimal parameter values giving the greatest value of GRG.

The study demonstrates that the process parameters of any dimple sheet manufacturing industry can be optimized, and the effect of process parameters can be identified.

The proposed low-cost model is relatively economical and readily implementable to small- and large-scale industries using newly developed multi-dimpling multi-punch and die.