Absolute Fit Indices Research Articles

Development and validation of an instrument to evaluate the perspective of using the electronic health record in a hospital setting

BackgroundEvaluating healthcare information systems, such as the Electronic Health Records (EHR), is both challenging and essential, especially in resource-limited countries. This study aims to psychometrically develop and validate an instrument (questionnaire) to assess the factors influencing the successful adoption of the EHR system by healthcare professionals in Moroccan university hospitals.MethodsThe questionnaire validation process occurred in two main stages. Initially, data collected from a pilot sample of 164 participants underwent analysis using exploratory factor analysis (EFA) to evaluate the validity and reliability of the retained factor structure. Subsequently, the validity of the overall measurement model was confirmed using confirmatory factor analysis (CFA) in a sample of 368 healthcare professionals.ResultsThe structure of the modified HOT-fit model, comprising seven constructs (System Quality, Information Quality, Information technology Service Quality, User Satisfaction, Organization, Environment, and Clinical Performance), was confirmed through confirmatory factor analysis. Absolute, incremental, and parsimonious fit indices all indicated an appropriate level of acceptability, affirming the robustness of the measurement model. Additionally, the instrument demonstrated adequate reliability and convergent validity, with composite reliability values ranging from 0.75 to 0.89 and average variance extracted (AVE) values ranging from 0.51 to 0.63. Furthermore, the square roots of AVE values exceeded the correlations between different pairs of constructs, and the heterotrait-monotrait ratio of correlations (HTMT) was below 0.85, confirming suitable discriminant validity.ConclusionsThe resulting instrument, due to its rigorous development and validation process, can serve as a reliable and valid tool for assessing the success of information technologies in similar contexts.

Factors for Reluctance of Individuals and Business Firms in Investing on Fixed Deposits in Tanzania

This study assess on the factors for reluctance of public in investing on fixed deposits. Either to reveal the facts three main hypotheses were formulated that is i) investing on fixed deposits in covenants borne with financial services is the reason for reluctance ii) the liquidity risks associated with investing on the fixed deposits is the factor that count for reluctance and iii) the risk sharing unawareness is the factor that count for public being reluctant in investing on fixed deposits account. Using a quantitative approach, explanatory research design and simple random sampling techniques the facts were gathered from (96) Bankers, Banking specialists and Investors in Financial services/products. The facts being collected by employing structured questionnaires and the cleaned through use of multiple imputations, the Augmented Dicker Fuller Unit root test, the Runs test, incremental fit index, absolute fit index were applied in analyzing data. Either the results were as follows: the covenants and more other requirements in investing on the fixed deposits accounts count for the reason over public reluctance. Moreover long cash conversion cycle that lead into liquidity risks was another reason found to be influencing factor for reluctance. Furthermore unawareness over risk sharing portfolio and maturity of fund invested opportunities was revealed to be another reason why there was reluctance. The study therefore recommends that Banks should act leanly and responsively towards meeting the need of investors.

Confirmatory factor analysis of the effect of daily-living on the happiness of community-dwelling older adults in Chile

This study examined the effect of the human-functioning dimension on happiness among community-dwelling older adults (OAs) in Chile. Questionnaires were used for data collection from a sample of 785 OAs of both sexes attending healthcare institutions. Exploratory factor analysis was performed using parallel analysis and oblique rotation. Confirmatory factor analysis and structural equation modeling were conducted using the maximum likelihood and unweighted least squares methods. Goodness-of-fit analyses were performed by considering absolute and respective incremental fit indices. The relationships between the functioning and happiness factors were all significant at the 1% level, indicating that functioning impacts happiness. The ratios of the variances between both constructs were identical to those of the covariances, indicating consistency between the models, with similarities and equalities in the estimation of their parameters. The modeling confirms a direct relationship between activities of daily living functioning and happiness. Given that a lack of functioning significantly affects OAs’ happiness and quality of life, this relationship is consistent with the available theory. These findings may contribute to the formulation of social and health policies regarding OAs in Chile and other Latin American countries.

Investigation of Zero Waste Management Behavior in Turkey

<p>Population growth, industrialization and technological advances have significantly increased waste production, waste collection, transportation and disposal induced significant costs. Waste could be considered a resource that allows nations to reduce the consumption of primary raw materials, protect natural resources and improve economic revenues by reducing environmental pressures. Zero waste approach for sustainable waste management has been approved and implemented globally. The present study aimed to investigate the basic zero-waste management in Turkey. Thus a scale that included eight items was developed and validity and reliability of the scale were analyzed. Explanatory factor analysis (EFA) was initially applied. Then, the fitness of the model was tested with confirmatory factor analysis (CFA). Cronbach's α (internal consistency coefficient) was calculated as 0.778. Chi-square test, RMSEA, GFI and CFI fit indices were employed in the first-order confirmatory factor analysis conducted on the whole sample. Chi-square was calculated as 1.310 in confirmatory factor analysis. Also, comparative and absolute fit indices were determined as follows: RMSEA=0.035, GFI=0.976, CFI=0.987, RMR=0.030. The analysis of the findings revealed that “Zero Waste Management Behavior” scale fit indices demonstrated excellent model fit for the findings. Furthermore, there were no significant differences between one-way ANOVA and T test results on participant statements based on gender, while there were significant differences between these variables based on age, education level and the region of residence.</p>

Designing and Validating the Model of Empowering Faculty Members to Encourage Students to Engage in Entrepreneurial Activities

The present research adopts an exploratory and foundational approach to provide a conceptual framework for explaining the empowerment of faculty members in encouraging students' entrepreneurial activities. The statistical population of the qualitative and quantitative sections of the study consists of all faculty members of Farhangian University, totaling 820 individuals. The qualitative sample was selected using snowball and purposive sampling methods, consisting of 15 participants, while the quantitative sample was determined using the Morgan table and random sampling, totaling 265 participants. The data collection instruments for the qualitative section were semi-structured interviews, and for the quantitative section, a researcher-made questionnaire was used. The content validity of the interview questions and questionnaire was confirmed by 5 faculty members of Farhangian University. The reliability of the interview results was calculated and confirmed using the Holsti test with a coefficient of 0.81, and the questionnaire reliability was assessed and confirmed using Cronbach's alpha coefficient, which was equivalent to 0.837. The data analyzed using MAXQDA 20.4.2, IBM SPSS Statistics 26, and SmartPLS 3 software. The data analysis conducted using a data-driven theoretical framework, which included open, axial, and selective coding to achieve the extracted pattern from the research. The qualitative findings indicate that 6 dimensions, 14 factors, and 62 components of empowering faculty members are effective in encouraging students' entrepreneurial activities. The quantitative findings confirm the extracted pattern. Absolute and parsimonious fit indices support the desired fit of the empowerment model of faculty members in encouraging students' entrepreneurial activities.

Selecting an Appropriate Cognitive Diagnostic Model for Eighth Grade Math Questions: TIMSS 2019 Study

Background and Objective: Traditional assessment methods have been criticized for neglecting the cognitive processes required by test-takers to provide correct responses. To address these issues, cognitive diagnostic models have been introduced by researchers. This study aimed to explore and apply cognitive diagnostic models to estimate the psychometric properties of published eighth-grade math questions from TIMSS 2019. Methods and Materials: The research method employed in this study is mixed-methods, conducted with a retrofitting approach to existing tests. The statistical population of this study consisted of all Iranian eighth-grade students who participated in the TIMSS 2019 study. The population size included 1,095,026 students from 23,895 schools. The sample consisted of 5,980 Iranian eighth-grade students from 220 schools, selected through a two-stage cluster sampling method, with an average age reported as 14.1 years. Findings: Using a qualitative method, 16 skills across four content domains—knowledge, application, and reasoning—were identified and the Q-matrix was developed. Then, in the quantitative section, the fit of the DINA model as a compensatory model, the DINO model as a non-compensatory model, and the G-DINA model as a general model were examined with data from students' responses to math questions in blocks 1, 2, 3, and 5. The findings showed that in terms of model fit, the G-DINA model had the weakest fit, while the DINA model had the best fit. Absolute fit indices of the models also indicated that the DINA model was better, and the DINO model did not fit. Relative fit indices of the models showed no significant difference between the G-DINA model and the other two models, and overall, the non-compensatory DINA model demonstrated the best fit. Conclusion: The results indicated that the DINA model had the best fit with the data and can be considered the optimal model for analyzing the eighth-grade mathematics questions in TIMSS 2019.

Study of Influential Factors That Affect LGBTQ’s Travel Destination Choice Decision

LGBTQ tourism is a very promising market with annual worldwide economic impact at more than 140 billion USD. The global LGBT tourism market is expected to reach US$ 568.5 Billion by 2030. The LGBT tourism market is important both economically and socially. LGBT travelers are often high-spending and frequent travelers, and their tourism dollars can have a significant impact on local economies. With the introduction of the 'pink dollar,' organizations hope to tap into members of the LGBTQ+ community who are willing to spend big. The 'pink dollar' refers to money spent by members of the LGBTQ community in the United States. It has boosted the US economy by billions of dollars. Additionally, by creating welcoming and inclusive travel experiences for LGBT individuals, the tourism industry can help promote greater acceptance and understanding of LGBT people around the world. LGBT travelers have unique needs and preferences when it comes to travel, and the tourism industry has responded with a range of products and services designed to meet these needs. These may include LGBT-friendly accommodation, events, and tours, as well as marketing and outreach efforts that specifically target the LGBT community.The purpose of this research is to study factors influencing LGBTQ’s Travel Destination Choice Decision. These factors include nine independent variables: Recommendations (RE), Popularity (PO), Destination Information (DI), Special Offers (SO), Nearness (NN), Amenities/Facilities (AF), Safety & Security (SS), Destination Features (DF), Strategic Fit (SF) and one dependent variable: Travel Behavior (TB). 400 sample were collected using electronic questionnaire through social media. We used Structural Equation Models (SEM) for data analysis. The result shows that since the RMSEA, which is an absolute fit index that assesses how far our hypothesized model is from a perfect model, for this model is .04 (<.05) which strongly indicates a “close fit” and the Goodness of Fit Index (GFI) value is .904 (>.90), the model seems to fit well according to the descriptive measures of fit. More importantly, Recommendations (RE) and Safety & Security (SS) are significantly defined as significant influential factors that affect LGBTQ’s travel destination choice decision due to their p-values are equal and less than .05. That means LGBTQ choose their travel destination if it’s strongly recommended by friends & family, online & social media, and customer positive review & sharing tips. Moreover LGBTQ prefer the destinations that offer personal safety and safe accommodation and destinations that they won’t be taken advantage of financially i.e. safe and sound destinations without being scammed.

Development of a Safety Heavy-Duty Vehicle Model Considering Unsafe Acts, Unsafe Conditions and Near-Miss Events Using Structural Equation Model

The World Health Organization has revealed that Thailand ranks first in Asia with regard to the region’s road traffic death rate. Due to the growth in the domestic economy and demands in logistics, traffic congestion regularly occurs and brings higher risks to transportation, resulting in a constant increase in the accident rate involving heavy-duty vehicles (HDVs), with a tendency to escalate in the future. To prevent its occurrence and solve the problem, this research aims to present a “Safety HDV Model” based on four dimensions, namely, driver behaviors, unsafe roadway environment, types of vehicles and near-miss events, which are all considered as causes of accidents. In this study, the researchers use the Delphi method to obtain a consensus from experts in logistics and safety from both public and private organizations, and then they define indicators and assess the complex dimensions. Based on the consensus, the researchers find 4 dimensions, 15 factors and 55 indicators with a high level of consensus at the Kendall’s coefficient of concordance (W) of 0.402 and P less than 0.001 to be relevant to safety in logistics. To estimate the influences among dimensions, the researchers apply a structural equation model and find that both absolute fit indices and incremental fit indices demonstrate good fit, with a CMIN/DF of 1.90, RMSEA of 0.048, GFI of 0.95, AGFI of 0.92 and RMR of 0.032 for the absolute fit indices and NFI of 0.97, CFI of 0.98, TLI of 0.98 and IFI of 0.98 for the incremental fit indices. As the model is consistent with data and variables, it is considered to be valid to be adopted by responsible authorities to improve unsafe roadway environments and behaviors of HDV drivers. As the data in the model can be altered by location, the model can be utilized as a tool in strategic planning and management to prevent accidents in each area of the country in the future.

Structural Equation Model-Based Selection and Strength Co-Relation of Variables for Work Performance Efficiency Under Traffic Noise Exposure

In this work, we integrated exploratory factor analysis (EFA) followed by structural equation modelling (SEM) to assess the work performance efficiency under the traffic noise environment for open shutter shopkeepers in the Indian urban context. 706 valid questionnaire responses by personal interviews in local language were collected from open shutter shopkeepers exposed to noise level (Leq) of 77 dBA for 12 to 14 hours daily. The questionnaire was prepared based on demographics, environmental conditions, and primary effects of noise pollution. Among which four common latent factors which summaries 17 questionnaire response items were obtained by exploratory factor analysis, which are “Impacts of noise” (IM), “Environmental conditions” (EC), “Personal characteristics” (PC) and “Work efficiency” (WE). The associations between the individual latent factors were studied by the structural equation model method in AMOS software. Validation of the constructed model was carried out by testing the proposed hypothesis as well as goodness-of-fit indices like Absolute fit, Incremental fit, and Parsimonious fit indices. The effect of specific latent factors derived on the work efficiency of shopkeepers in the noisy area was characterized by the path coefficients estimated in the SEM model. It was found that work performance efficiency (WE) was greatly influenced by the primary impacts of noise pollution like annoyance, stress, interference in spoken communication, which was associated with the latent factor “Impacts of noise” (IM) with a path coefficient of 0.931. The second latent factor “Environmental conditions” (EC), which was associated with parameters like ambient temperature and humidity, showed less path coefficient of 0.153. And lastly, a latent factor called “Personal characteristics” (PC) associated with age, experience, education, showed the least path coefficient of 0.05. The work efficiency of open shutter shopkeepers working in a highly noisy commercial area is profoundly affected by the prominent effects of noise pollution and least affected by ambient environmental conditions as well as their personal characteristics. The developed model clarified some casual relationships among complex systems in the study of noise exposure on individuals n tier 2 cities in the Indian context and may help other researchers to study of tier I and tier III cities.

Study of Factors Influencing Consumer to Adopt Cryptocurrency

Cryptocurrencies have become one of the most traded financial assets in the last decade. The first half of 2022 will not be easily forgotten by the crypto faithful as it shifted investor sentiment after a monster cryptocurrency rally in 2021 that saw bitcoin (BTC) and altcoins scale multiple record peaks. Just as users were getting comfortable with their passive DeFi incomes, Terra and its non-collaterised stablecoin collapsed in a sensational turn of events. Three Arrows Capital (3AC), a crypto hedge fund that managed assets worth about $10bn at its peak, went bankrupt as falling crypto prices forced liquidations of collateralized loans and leveraged trading positions across the industry, aggravating the sell-off. Investor sentiment plummeted as crypto lending platforms like Celsius and Babel suspended withdrawals for clients. Investor sentiment plummeted as crypto lending platforms like Celsius and Babel suspended withdrawals for clients. The future of cryptocurrency still remains unclear. Given all these mess, we would like to know what factors could actually influence consumer adoption of cryptocurrency from here on out.The purpose of this research is to study factors influencing consumer to adopt cryptocurrency. These factors include seven independent variables: Convenience (CV), Popularity (PL), Usefulness (UF), Credibility (CD), Recommendations (RC), Price Stability (PS) and Risk (RK) and one dependent variable: Crypto Behavior (BH). 400 sample were collected using electronic questionnaire through social media. We used Structural Equation Models (SEM) for data analysis. The result shows that Since the RMSEA, which is an absolute fit index that assesses how far our hypothesized model is from a perfect model, for this model is .047 (<.05) which strongly indicates a “close fit” and the Goodness of Fit Index (GFI) value is .934 (>.90), the model seems to fit well according to the descriptive measures of fit. More importantly Popularity (PL), Usefulness (UF), Credibility (CD), Recommendations (RC) and Risk (RK) seem to have significant effects on influencing consumer to adopt cryptocurrency due to their p-values are both less than .05. That means as long as cryptocurrency is becoming more popular, useful, credible and highly recommended by key stakeholders (friends & family, professionals and influencers) with better risk management, consumer will be more welcome to adopt cryptocurrency as both day-to-day currency and investment alternative. One interesting finding is that Price Stability (PS) of cryptocurrency that we’ve seen over and over again especially post-covid doesn’t seem to significantly impact much of consumer adoption.

Study of Factors Influencing Consumers to adopt EVs (Electric Vehicles)

Statista market forecast has shown that revenue in the Electric Vehicles market is projected to reach US$457.60bn in 2023. Revenue is expected to show an annual growth rate (CAGR 2023-2027) of 17.02%, resulting in a projected market volume of US$858.00bn by 2027. Electric vehicles are gaining popularity and market share. In Q2 2022, EV sales accounted for 5.6% of the total auto market (up from 2.7% in Q2 of 2021). Clean energy and improved performance are driving people to make the switch to electric. Government incentives continue to buttress the future of all things electric. Between today and 2050, the electric vehicle market opportunity is slated to rise to $53 trillion. Additionally, global electricity demand in that same year is projected to increase by 27% even though KPMG reports in December 2022 that automotive executives are less bullish than they were last year about the adoption of electric vehicles. The survey found 76% are concerned that inflation and high interest rates will adversely affect their business in 2023. For the U.S., the median expectation for EV sales was 35% of the new vehicle market by 2030 — down from 65% a year earlier.The purpose of this research is to study factors influencing consumer to adopt Electric Vehicles. These factors include seven independent variables: Mileage (ML), Battery (BA), Charge station (CS), Price (PR), Maintenance (MT), At-home charger (HC), Government policy (GP), Promotion (PM), Brand (BR) and one dependent variable: Consumer Factor (CF). 387 sample were collected using electronic questionnaire through social media. We used Structural Equation Models (SEM) for data analysis. The result shows that since the RMSEA, which is an absolute fit index that assesses how far our hypothesized model is from a perfect model, for this model is .04 (<.05) which strongly indicates a “close fit” and the Goodness of Fit Index (GFI) value is .913 (>.90), the model seems to fit well according to the descriptive measures of fit. More importantly, Battery (BA), Charge station (CS), Price (PR), At-home charger (HC), Government policy (GP), and Brand (BR) are significantly defined as key success factors of electric vehicles (EV) from the consumer’s perspective due to their p-values are all less than .05. That means if these significant factors are taken good care of from having branded EV with quality battery at a competitive price offering with more charge stations available both in public and at home, all of which are deeply supported by government policy to encourage people to switch over to EV ASAP, EV will become much more successful and widely adopted from consumer standpoint much faster than what has been happening so far.

Study of Factors Influencing Sustainable Supply Chain Management (SSCM) in China

Sustainability in the transportation and supply chain industry has been a concern for decades. Conversations have been ongoing about how to reduce the carbon footprint, incorporate electric vehicles into fleets, and adopt alternative fuels. Now, however, we’re at a crossroad. The global climate crisis has reached a tipping point, highlighting transportation’s contribution to the problem in the boardrooms of most corporations. And for good reason. According to the U.S. Environmental Protection Agency, transportation is responsible for nearly a third (29%) of all greenhouse gas emissions. While passenger vehicles make up a significant portion of that number, ships, trains, planes and freight trucks are also in the mix.The purpose of this research is to study factors influencing Sustainable Supply Chain Management (SSCM) in China. These factors include seven first-order variables: independent variables: Carbon Footprint (CF), Organizational Practices (OP), Transportation Model (TM), Environmentally Responsible Packages (EP), Alternative Energy (AE), Partnership Initiative (PI), and Technology Development (TD); two second-order variables: Environmental, Social, and Governance (ESG) and Operating Model (OPER) and one dependent variable: Sustainable Supply Chain Management (SSCM). 400 sample were collected using electronic questionnaire through social media. We used Structural Equation Models (SEM) for data analysis. The result shows that since the RMSEA, which is an absolute fit index that assesses how far our hypothesized model is from a perfect model, for this model is .039 (<.05) which strongly indicates a “close fit” and the Goodness of Fit Index (GFI) value is .903 (>.90), the model seems to fit well according to the descriptive measures of fit. Moreover, CFI, which is incremental fit indices that compare the fit of our hypothesized model with that of a baseline model (i.e., a model with the worst fit), its value equals .956 indicating an acceptable fit.

Study of Factors Influencing Digital Transformation Process in Bangkok

Most digital transformations fail. Various studies from academics, consultants, and analysts indicate that the rate of digital transformations failing to meet their original objectives ranges from 70% to 95%, with an average at 87.5%. Yet, digital transformation has been at the top of corporate agendas for at least a decade and shows no sign of slowing down. On the contrary, many commentators have highlighted the accelerating impact of the Covid-19 period on digital transformation. Digital transformation is something no management team should attempt alone. It takes deep into reality and a sense of ownership among people across the enterprise to make transformation a reality.The purpose of this research is to study factors influencing digital transformation process in Bangkok, Thailand. These factors include seven first-order independent variables: Leadership (LD), Employee (EP), Culture (CT), Work Environment (WE), Mindset (MS), Organizational Friction (OF), Management of Transformation (MT), and Talent Acquisition (TA); three second-order variables: Leadership & Motivation (LDM), People (PPL), and Workplace Culture (WPC) and one dependent variable: Digital Transformation (DT). 400 sample were collected using electronic questionnaire through social media. We used Structural Equation Models (SEM) for data analysis. The result shows that since the RMSEA, which is an absolute fit index that assesses how far our hypothesized model is from a perfect model, for this model is .04 (<.05) which strongly indicates a “close fit” and the Goodness of Fit Index (GFI) value is .902 (>.90), the model seems to fit well according to the descriptive measures of fit. Moreover, CFI, which is incremental fit indices that compare the fit of our hypothesized model with that of a baseline model (i.e., a model with the worst fit), its value equals .903 indicating an acceptable fit.More importantly, Talent Acquisition (TA), Leadership & Motivation (LDM), and Workplace Culture (WPC) seem to have significant effects on Digital Transformation (DT) process due to their p-values are all less than .05. That means if corporates focus more on acquiring new talent and at the same time improve corporate leadership and motivation and workplace culture, they will be more likely to be successful in digital transformation which is necessary condition for all organizations to be competitively sustainable going forward.

The Effect of Organizational Commitment as Mediator on Relationship between Training and Work Performance

This study aims to identify the effect of organizational commitment as a mediator on the relationship between training and work performance at Community Health Centers for midwives in Banyumas Regency, Central Java, Indonesia. A quantitative research design was selected to answer four research questions. Data collection uses a questionnaire with a five-point Likert scale. This study involved 243 midwives of Puskesmas in Banyumas Regency as a sample using a systematic random sampling method to calculate the sample size of Puskesmas. After that, proportional random sampling was used to determine the sample size of Puskesmas midwives. Data analysis used the Structural Equation Modeling method. The findings of the study show that based on the Measurement Model test, the Loading Factor value from all dimensions of the research construct is greater than 0.5. It means that they can be explained by each dimension of it. The results of the Structural Model exams show good results for absolute fit indices, incremental fit indices, and parsimonious fit indices values, which means that the measurement model and Structural Model used are built to fit. It is concluded that training has a significant positive effect on work performance, training has a significant positive effect on organizational commitment, organizational commitment has a significant positive effect on work performance, and organizational commitment moderates the relationship between training and work performance.

Factors Influencing Customer Choice Decision of Property Management Company in Bangkok Thailand

Property Management, when done efficiently, is a solution to cutting costs and expenses associated with buildings and increasing the value of assets. Property Management is the management of assets and real property, either for residential or commercial purposes. It is a crucial task enabling a real estate development project to be met with success. These companies are supposed to oversee and manage shared assets, the common area, the building systems and the environment at a project for the purpose of keeping the project in good condition, attractive and orderly – doing so ultimately adds value in the long run. Property management companies have become an important factor for living at a residential project and for ensuring a project’s sustainability. Therefore choosing the right property management team helps to make your project or your home more liveable, valuable and sustainable. The purpose of this research is to study factors influencing Customer Choice Decision of Property Management Company in Bangkok Thailand. These factors include six independent variables: Brand Trust (BT), Track Record (TR), Property Characteristics (PC), Corporate Staff (CS), Management System (MS), and Management Fee (MF)and one dependent variable: Choice Behavior (CB). 400 sample were collected using electronic questionnaire through social media. We used Structural Equation Models (SEM) for data analysis. The result shows that Since the RMSEA, which is an absolute fit index that assesses how far our hypothesized model is from a perfect model, for this model is .043 (.90), the model seems to fit well according to the descriptive measures of fit. More importantly Management Fee (MF) and Property Characteristics (PC) seem to have significant effects on influencing Customer Choice Decision of Property Management Company in Bangkok Thailand due to their p-values are both less than .05. That means other than management fee that seems obvious, Thai consumer also look at whether Property Management Company has relevant experiences in managing similar kind of property before be it large property, hi-end property, or mix-use property complex.

STUDY OF FACTORS INFLUENCING CHINESE PURCHASE DECISION OF IMPORTED PRODUCTS FROM THAILAND

The global economy and supply chain has suffered a severe shock since the outbreak of the COVID-19 pandemic. As a result, exports from major countries in Asia such as Japan and South Korea to China grew significantly. However, the popularity of imported products from Thailand to China has always increased with or without COVID-19 pandemic. Therefore, it might be interesting to know why Chinese have been in love with imported products from Thailand for so long whereas many imported items from Thailand can also be found elsewhere especially in other Asian countries. The purpose of this research is to study factors influencing Chinese purchase decision of imported products from Thailand. These factors include six independent variables: Product (PD), Rareness (RN), Affordability (AF), Credibility (CD), Proximity (PX), and Culture (CT), and one dependent variable: Purchase Decision (PC). 433 sample were collected using electronic questionnaire through social media. We used Structural Equation Models (SEM) for data analysis. The result shows that the RMSEA, which is an absolute fit index that assesses how far our hypothesized model is from a perfect model, for this model is .028 (.90), the model seems to fit well according to the descriptive measures of fit. On the contrary, CFI and TLI, which are incremental fit indices that compare the fit of our hypothesized model with that of a baseline model (i.e., a model with the worst fit), whose values are both greater than .90 (CFI = .984, TLI = .980) indicating an acceptable fit. More importantly all factors included in the model i.e. Product (PD), Rareness (RN), Affordability (AF), Credibility (CD), Proximity (PX), and Culture (CT) seem to have significant effects on influencing Chinese purchase decision of imported products from Thailand due to their p-values are all less than .05.

Development of the Acoustic Comfort Assessment Scale (ACAS-12): Psychometric properties, validity evidence and back-translation between Spanish and English.

A methodological proposal of a scale for human perception assessment of acoustic environment (acoustic comfort) in urban public spaces is presented: Acoustic Comfort Assessment Scale (ACAS 12). This paper shows the process of constructing this scale and its psychometric properties and validation. The approach is based on the soundscape conceptualisation collected in ISO 12913-1:2014 (Acoustics-Soundscape: Definition and conceptual framework). The proposed scale is a 5-point semantic differential scale made up of twelve pairs of bipolar adjectives, grouped around five theoretical dimensions. It is based on previous versions of 2-point and 3-point scales. The ACAS-12 scale is the result of several empirical studies carried out by the authors on environmental and acoustic comfort assessment, beginning in 2011 in some cities of the Basque Country (Spain). Selected urban open places cover a wide variability of acoustic and non-acoustic characteristics, as well as the type of participants (real users) and activities carried out in these places. The original language of this scale is Spanish. The back-translation technique has been applied to create the English version. The results highlight the good psychometric properties of the ACAS 12 scale (Cronbach's alpha 0.91 and composite reliability 0.90) and indicate that the best factorial solution is that of a single factor composed of the 12 pairs of adjectives that would explain approximately 50% of the ACAS-12 variance (44% of extracted variance by Raykov's method or 56% by the MAP test). The absolute and incremental fit indices were above the minimum reference value of 0.90 and the residual-based indices showed values close to suitability (SRMR = 0.057; RMSEA = 0.042). This result supports the consideration of the ACAS-12 scale as a general measure of acoustic comfort.

LEARNING RESOURCES DIGITALIZATION FOR ELEMENTARYELEMENTARY SCHOOL STUDENTS

This study aims to determine the effect of using gadgets and theparental consent to the digitalization of learning resources for elementary school students. The research method uses the associative quantitative approach which aims to determine the relationship between two or more variables. The research sample was 10478 respondents from superior accredited elementary schools in Jakarta, Bogor, Depok, Tangerang and Bekasi. The research was conducted from October to November 2022. The data are collected by the means of questionnaires, and analyzed with covariance-based Structure Equation Modeling (CB-SEM). Model evaluation is carried out through two stages of testing, namely testing the measurement model with Confirmatory Factor Analysis (CFA) and testing the overall fit of the model with Absolute Fit Indices, Incremental Fit Indices, and Persimonious Fit Indices. Both tests produce acceptable values when compared to reference values. Thus the overall SEM model fits the sample data. Testing the structural model shows that the use of gadgets has a direct effect, while the parental consent has an indirect effect through the use of gadgets on the digitalization of learning resources for elementary school students.

Factors Influencing Online Shopping behavior of Online Fashion Retailers

The COVID-19 pandemic has changed online shopping behaviors, according to a survey of about 3,700 consumers in nine emerging and developed economies. As people embraced social distancing, they turned to online shopping more than ever before. The fluctuation of the COVID-19 pandemic and the ways it influences and modifies our shopping habits will likely continue into the foreseeable future. Without a doubt, the COVID-19 pandemic forced everyone to change the way they shop. Whether you were a fan of online shopping or in-store browsing, the pandemic altered routines in many obvious, and some not so obvious. Therefore to understand what now actually affects consumer purchase behavior when it comes to online shopping is key to success for many e-tailers especially for those online fashion retailers which is the main focus of this study.The purpose of this research is to study factors influencing online shopping behavior of online fashion retailers (apparel, fashion accessories, shoes). These factors include eight independent variables: Web design(WD), Reputation(RP), Web contend(WC), Brand effect(BR), Product(PD), Service(SV), Price(PR), and Promotion (PM), and one dependent variable: Online shopping behavior (OB). 437 sample were collected using electronic questionnaire through social media. We used Structural Equation Models (SEM) for data analysis. The result shows that the RMSEA, which is an absolute fit index that assesses how far our hypothesized model is from a perfect model, for this model is .027(.90), the model seems to fit well according to the descriptive measures of fit. On the contrary, CFI and TLI, which are incremental fit indices that compare the fit of our hypothesized model with that of a baseline model (i.e., a model with the worst fit), whose values are both greater than .90 (CFI = .981, TLI = .977) indicating an acceptable fit. More importantly almost all factors included in the model except Web contend (WC) i.e. Reputation(RP), Web design(WD), Brand effect(BR), Product(PD), Service(SV), Price(PR), and Promotion(PM) seem to significantly affect online shopping behavior of online fashion retailers due to their p-values are all less than .05.

Comparison and convergent validity of five Mediterranean dietary indexes applied to Brazilian adults and older adults: data from a population-based study (2015 ISA-Nutrition).

Different dietary indexes are proposed to investigate adherence to the Mediterranean diet (MD). However, they are based on different methodologies, and limited research has compared them to each other, particularly in non-Mediterranean populations. We aimed to compare five indexes intended to measure adherence to the MD. The sample was composed of adults and older adults (n 1187) from 2015 ISA-Nutrition, a cross-sectional population-based study in São Paulo, SP, Brazil. Dietary data obtained through two 24-h dietary recalls (24HDR) from which the Mediterranean diet scale (MDS), Mediterranean diet Score (MedDietscore), Mediterranean dietary pattern (MDP), Mediterranean Adequacy Index (MAI) and Mediterranean-Style Dietary Pattern Score (MSDPS) were calculated. The correlations and agreements between them were analysed by Spearman's correlation and linearly weighted Cohen's Kappa coefficients, respectively. Confirmatory factor analyses (CFAs) were applied to investigate their convergent validity. The highest correlations were found between MDP and MAI (r = 0⋅76; 95% CI 0⋅74-0⋅79) and between MDP and MDS (r = 0⋅72; 95% CI 0⋅69-0⋅75). The greatest agreements observed were moderate, between MDP v. MAI (κ = 0⋅57, P < 0⋅001) and MDP v. MDS (κ = 0⋅48, P < 0⋅001). The goodness-of-fit of CFA for MedDietscore (RMSEA = 0⋅033, 90% CI 0⋅02-0⋅042; SRMR = 0⋅042) and MSDPS (RMSEA = 0⋅028, 90% CI 0⋅019-0⋅037; SRMR = 0⋅031) had acceptable values for absolute fit indices. Vegetables, olive oil, MUFA:SFA ratio and cereals with legumes were more relevant to characterise the MD (factor loadings ≥0⋅50). The MDS, MAI and MDP classified the population similarly, but the MedDietscore showed better performances in evaluating adherence to the MD. These results provided guidance for the most appropriate Mediterranean dietary index to be applied in non-Mediterranean populations.