Integrating big data and marketing concepts into tourism, hospitality operations and strategy development.

Big data (BD) research articles are on new issues, this study sought to fill the knowledge gap of linkage the relationships between big data and marketing strategy with comprehensive viewpoints across different research fields in tourism and hospitality literatures. Content analysis was conducted to gather materials from the particular studies. For each study, the content analysis included the title, abstract, journal, type of sample, exploration design, statistical and analytical techniques, data collection process and keywords was also conducted to confirm the main results of the criteria. The research shows that big data adds value to marketing strategies by using social media to collect information from consumers, which is complemented with appropriate evidence relevant to predicting their needs and behaviors.

Levels of Phthalates, Bisphenol-A, Nonylphenol, and Microplastics in Fish in the Estuaries of Northern Taiwan and the Impact on Human Health.

Due to the sparsity in knowledge, we investigated the presence of various estrogenic endocrine-disrupting chemicals (EEDCs), including phthalates (PAEs), bisphenol-A (BPA), and nonylphenol (NP), as well as microplastics (MPs) in samples of the most widely consumed fish collected from different estuaries in northern Taiwan. We then proceeded to determine the likely contribution that this exposure has on the potential for health impacts in humans following consumption of the fish. Six hundred fish caught from five river estuaries (producing 130 pooled samples) were analyzed to determine how different factors (such as the river, benthic, pelagic, and migratory species) influence EEDCs' contamination and the possible impacts on human health following typical consumption patterns. The predominant EEDCs was diethyl phthalates (DEP), bis (2-ethylhexyl) phthalates (DEHP), and di-iso-nonylphthalate (DINP) in fish, present at 52.9 ± 77.3, 45.3 ± 79.8, and 42.5 ± 79.3 ng/g dry weight (d.w.), respectively. Residual levels of NP, BPA, and MPs in the fish were 17.4 ± 29.1 and 1.50 ± 2.20 ng/g d.w. and 0.185 ± 0.338 mg/g d.w., respectively. EEDCs and MPs levels varied widely among the five river estuaries sampled due, in part, to differences in habitat types and the associated diversity of fish species sampled. For DEP, the Lao-Jie River and pelagic environments produced the most severely contaminated fish species, respectively. DEP residues were also associated with the burden of MPs in the fish. Based on our analysis, we predict no substantial direct human health risk by EEDCs based on typical consumption rates of estuarine fish by the Taiwanese people. However, other sources of EEDC exposure cannot be ignored.

Risk perception and response toward climate change for higher education students in Taiwan.

The effects of climate change have already begun to threaten biological diversity and human societies, and these effects will continue to grow over time. To face the challenges related to mitigation and adaptation will require an educated and motivated citizenry. From the perspective of green education, providing knowledge related to climate change and promoting pro-environmental behaviors is imperative. In this study, we assess current levels of knowledge, risk perception and types of pro-environmental behaviors. We administered a questionnaire to 1118 university students in Taiwan evaluating climate change knowledge (15 items), risk perception (23 items), and attitudes toward behavioral change (33 items). Factor analyses were conducted to identify the underlying latent variables for risk perception and obstacles to behavioral change, and ANOVA tests were performed to identify significant associations between three different levels of climate change knowledge and responses to the risk perception and obstacles-to-change items. We found that higher levels of knowledge significantly predicted greater perceptions of risk related to biodiversity threats and increased public costs. In terms of behavior, students with lower levels of knowledge were significantly more likely to find uncertainties related to climate change to be a greater obstacle to engaging in pro-environmental behaviors. Higher levels of knowledge clearly allow individuals to better assess the threats posed by climate change and reduces the perceived level of uncertainty related to climate change and the impact of pro-environmental behaviors. Our results suggest that Taiwan's efforts to implement climate change related information in the public schools and in the university system have been effective and that such efforts should be broadened to reach the public as a whole.

An innovative environmental citizen behavior model: Recycling intention as climate change mitigation strategies.

Climate change has been led to the increasing magnitude of frequency and severity of extreme weather, causing serious damage to overall economy of a country and individual economy of enterprises. Only relying on government subsidy to combat the impact of climate change, the work could be in vain. This study tries to use recycling action as mitigation strategies of climate change, and analyze the dominant influencing factors that affect recycling intention. This study stands in the perspective of green education in universities, regards the recycling intentions in response to climate change as pro-environmental behaviors, and revises and verifies the citizen pro-environmental behavior model. The green educations programs can communicate, promote, active and internalize the environmental citizenship through resource recycling intentions. Students learn about the importance of advocacy and activism to environmental citizenship, then generate environmentally sustainable and recycling intentions, and achieve the goal of improving the environment and mitigating to climate change.

A pro-environmental behavior model for investigating the roles of social norm, risk perception, and place attachment on adaptation strategies of climate change.

Today's climate change is a major problem and challenge for the global environment and human civilization, and it can lead to dramatical floods over specific regions. As climate change intensifies, climate change adaptation strategies, such as flood insurance, energy taxes, and other risky financial strategies, have drawn worldwide attention and discussion. Risk control methods have been widely used to mitigate the impact of climate change on past flood losses, but past risk control strategies on climate change have not focused on the exploration of the relationship between environment, society, and humans. Based on the theoretical model of pro-environmental behavior, this study compares and analyzes four theoretical models and proposes a modified competitiveness model to effectively predict the pro-environmental behavior of college students with partial least squares (PLS) manner. Social norm could play a dominant role of mediator between risk perception, place attachment, and pro-environmental behavior. Although risk perception and local attachment are positively related to risk financial strategy, the promotion of social norms will increase the intention of risk financial strategy. For intention of risk financial strategies within pro-environmental behavior, the efficiency of enhancing local attachment was higher than that of risk perception.

Application of Escherichia coli antibiotic resistance patterns for contamination source identification in watershed.

Spatial correlation of pollution of the water resource in Taipei, Taiwan, were examined by analyzing the antibiotic resistance patterns (ARPs) of 96 Escherichia coli colonies, which were isolated from 7 sampling sites in 3 river sections. The ARPs were the growth patterns of isolated E. coli colonies in the medium with seven kinds of antibiotics, including ampicillin, chlortetracycline, erythromycin, oxytetracycline, streptomycin, tetracycline, and salinomycin of different concentrations. The results showed that the survival rate of E. coli decreased with increasing concentration of antibiotics; however, various ARPs under different antibiotics of different concentrations significantly increased both the useful information and complexities. Hierarchical cluster analysis (HCA) and two-stage principal component analysis (PCA) were applied to analyze the spatial correlations and interrelations of distinct ARPs among sampling sites in this study. It was found that the seven sampling sites can be categorized into three groups which may represent three possible pollution characteristics.

Towards an East Asian model of climate change awareness: A questionnaire study among university students in Taiwan.

East Asia emits more greenhouse gases into the atmosphere than any other region, yet little is known about attitudes towards climate change in this region. A cross-sectional survey investigating climate change knowledge, concern and behavior change was administered to 1118 university students at nine universities across Taiwan in June 2016. Knowledge was assessed with a 15-item quiz while concern and behavioral change were self-reported on 5-point Likert scales. The relationship of these three variables with various socio-demographic variables was investigated through Kruskal-Wallis tests and ordinal logistic regressions. Knowledge was homogeneous by region but differed sharply by socioeconomic position. Concern appears high by international standards, with 65% reporting being "somewhat concerned" and 28% being "very concerned," while climate change denial was negligible. Students expressing greater concern were more likely to be from eastern and southern Taiwan, regions more vulnerable to extreme weather events. However, these high concern levels did not translate into action, as only 38% of respondents reported "some" and 11% reported "very much" behavioral change in response to climate change. Higher levels of behavioral change were reported by students expressing greater concern and students with lower levels of climate change knowledge. In contrast with studies of Western societies, our findings suggest an East Asian model in which the conflict between economic growth and the environment is playing out in different ways, such that the crucial need is for policy leadership and not more education.

The Moderating Effects of Students' Personality Traits on Pro-Environmental Behavioral Intentions in Response to Climate Change.

This study developed a model that examined the relationship between undergraduate students' beliefs, norms and pro-environment behavioral intentions in the context of global climate change (GCC). The model was further evaluated to determine whether latent variables, such as sustainability value, environmental concern, social norms, perceived risk, pro-environmental attitude, as defined by the theory of planned behavior and value-belief-norm theory, significantly influenced students' intentions towards pro-environmental behavior. The research model was empirically tested using data collected form 275 undergraduate students. Empirical results found support for four interaction effects of personality traits and the related latent variables of environmental attitude, including sustainability value, social norms, environmental concern and perceived risk. The impact of undergraduate students' environmental attitudes was moderated by personality traits. The findings of this research offer policy makers and enterprises better understandings of undergraduate students' attitudes and behavioral intentions towards GCC and promote the visibility of this issue.

Establishment of a structural equation model for ground-level ozone: a case study at an urban roadside site.

This study established a cause-effect relationship between ground-level ozone and latent variables employing partial least-squares analysis at an urban roadside site in four distinct seasons. Two multivariate analytic methods, factor analysis, and cluster analysis were adopted to cite and identify suitable latent variables from 14 observed variables (i.e., meteorological factors, wind and primary air pollutants) in 2008-2010. Analytical results showed that the first six components explained 80.3 % of the variance, and eigenvalues of the first four components were greater than 1. The effectiveness of this model was empirically confirmed with three indicators. Except for surface pressure, factor loadings of observed variables were 0.303-0.910 and reached statistical significance at the 5 % level. Composite reliabilities for latent variables were 0.672-0.812 and average variances were 0.404-0.547, except for latent variable "primary" in spring; thus, discriminant validity and convergent validity were marginally accepted. The developed model is suitable for the assessment of urban roadside surface ozone, considering interactions among meteorological factors, wind factors, and primary air pollutants in each season.

Submicron particle characteristics of atmospheres in a long highway tunnel.

This study used a scanning mobility particle sizer (SMPS) to measure and categorize submicron atmospheric particles in the 14-737-nm size range for ambient and urban roadside air and for air in the Hsuehshan Tunnel (12.9 km), Taiwan. Principal component analysis, traffic flow, and particle size distributions were used to identify the emission characteristics of light-duty vehicles (LDV) with the SMPS data. In the Hsuehshan Tunnel, the particle size from the majority of emissions discharged by LDV is approximately 20-60 nm, and the maximum particle number can reach up to 2.5 × 10(5). In contrast, submicron particle size distribution for urban roadsides is mostly 14-200 nm, and the maximum particle number is approximately 4 × 10(4) with the particle number for most particle sizes being below 1,200. The submicron particle size distribution at the ambient air station was unimodal with a mode sizes at 30-50 nm with the maximum particle number of 3,000.

Development of real-world driving cycles and estimation of emission factors for in-use light-duty gasoline vehicles in urban areas.

This investigation adopts vehicle tracking manner to establish real-world driving patterns and estimates emission factors with dynamometers with 23 traffic-driving variables for 384 in-use light-duty passenger vehicles during non-rush hour. Adequate numbers of driving variables were decided with factor analysis and cluster analysis. The dynamometer tests were performed on FTP75 cycle and five local driving cycles derived from real-world speed profiles. Results presented that local driving cycles and FTP75 cycle were completely different in driving characteristic parameters of typical driving cycles and emission factors. The highest values of emission factor ratios of local driving cycle and FTP75 cycle for CO, NMHC, NO x , CH4, and CO2 were 1.38, 1.65, 1.58, 1.39, and 1.14, respectively.

Identification of source regions of PM10 with backward trajectory-based statistical models during PM10 episodes.

This study applies backward trajectory-based statistical techniques, residence time, conditional probability and emission attraction to evaluate potential source regions of PM10 over a coastal region. PM10 episodes were selected by principal component analysis for 1998-2005 over the Kaoping air quality basin. Residence time was applied to identify potential regions in which air parcels would remain over their 6- and 12-h trajectories. Emission attraction and conditional probability were used to analyze contribution ratios of distinct emission sources to air quality stations. The PM10 episodes screen 175 days (6 % of total days) and 35.9 % of total station numbers. Residence time and emission attraction clearly identified potential areas in which backward trajectories remained during PM10 episodes and high PM10 events. Emission attraction evaluated relative contributions of various sources (stationary, line, and area) from specific jurisdictions, and provided information on specific sources for high-priority PM10 emissions reduction. The conditional probabilities of emission attraction during high PM10 events show that high values concentrated near stationary and area sources in the city of Kaohsiung.

Spatiotemporal features of severe air pollution in northern Taiwan.

BACKGROUND, AIMS AND SCOPE: This research attempted to identify the dominant factors simultaneously affecting the airborne concentrations of five air pollutants with principal component analysis and to determine the meteorologically related parameters that cause severe air-pollution events. According to the definition of subPSI and PSI values through the U.S. EPA, the historical raw data of five criteria air pollutants, SO2, CO, O3, PM10 and NO2, were calculated as daily subPSI values. In addition to the airborne concentrations, this study simultaneous collected the surface meteorological parameters of the Taipei meteorological station, established by the Central Weather Bureau. METHODS: Principal component analysis was conducted to screen severe air pollution scenarios for five air pollutants: SO2, CO, O3, PM10 and NO2. The concentrations of various air pollutants measured at 17 air-quality stations in northern Taiwan from 1995 to 2001 were transformed into daily subPSI values. The correlation analysis of the five air pollutants and four meteorological parameters (wind speed, temperature, mixing height and ventilation rate) were included in this research. After screening severe air pollution scenarios, this study recognized the synoptic patterns easily causing the severe air-pollution events. RESULTS AND DISCUSSION: Analytical results showed that the eigenvalues of the first two principal components for SO2, CO, O3, PM10 and NO2 were greater than 1. The first component of five air pollutants explained 64, 64, 67, 76 and 63% of subPSI variance for SO2, CO, O3, PM10 and NO2, respectively. Only the correlation coefficient of NO2 and CO had statistically significant positive values (0.82); other pollutant pairs presented medium (0.4 to 0.7) or low (0 to 0.4) positive values. The correlation coefficients for air pollutants and three meteorological parameters (wind speed, mixing height and ventilation index) were medium or low negative values. In northern Taiwan, spring was most likely induced high concentrations and the component scores of the first component for SO2, CO, PM10 and NO2; summer was the worst season that caused high O3 episodes. Consequently, the analytical results of factor loadings for the first principal component and emission inventory of various sources revealed that mobile sources were dominant factors affecting ambient air quality in northern Taiwan. CONCLUSION: According to the results of principal component analysis for the five air pollutants, the first two of 17 components were cited as major factors and explained 71% of subPSI variance. Based on the inventory of NOx emissions and the isopleth diagram of factor loading for the first component, mobile sources in the southwest Taipei City accounted for the highest factor loading values and emission inventory values. Synoptic analysis and principal component analysis demonstrated that three types of weather patterns (high-pressure recirculation, prefrontal warm sector and the southwesterly wind system) easily caused the severe air-pollution scenarios. In summary, if severe air-pollution days occurred, the average meteorological parameters experienced adverse conditions for diffusing air pollutants; that is, the average values of wind speed, mixing height and ventilation index were lower than 2.1 ms(-1), 360 m and 800 m2s(-1), respectively. If one of the three synoptic patterns were to occur in combination with adverse meteorological conditions, severe air-pollution events would be developed. RECOMMENDATION AND OUTLOOK: By utilizing synoptic patterns, this work found three weather systems easily caused severe air-pollution events over northern Taiwan. Analytical results showed, respectively, the wind speed and mixing height were less than 2.1 m/s and 360 m during severe air-pollution events.

Spatial and temporal features of ambient air-quality over Taiwan.

This study analyzed a time series data of daily subPSI values to determine the temporal and spatial characteristics of ambient air-quality in Taiwan by employing the Varimax rotational method. The first and second rotational principal components nearly accounted for 54 and 19% for the variation of subPSI values over Taiwan. The factor loadings between rotational principal components and subPSI values of air pollutants identified the first and second components as combustion and photochemical sources, respectively. Time series analysis of rotational component scores associated with regions showed the relationships between rotational components and subPSI values and also revealed the optimum simulation period for combustion and photochemical sources.