Factors affecting brand preference in passenger car buying in Nepal

Awareness of healthy food, population growth, increasing incomes, and urbanization raise the global demand for fruit, where the second position goes to apples. However, their supply is insufficient, implying the lost revenues and exacerbating nutritional food insecurity. To help growers, traders, and consumers cope with such a challenge, this research focused on revealing some world patterns in apple production and trade detailed by groups of countries, their capacities, and prices. The explored data on fresh and processed apples derived from the Food and Agriculture Organization Statistics. The methodological framework of the study engaged divisive hierarchical clustering, analysis of interval variation series, and inequality indicators. The research findings identified two major clusters of 50 out of 96 countries specialized in production and foreign sales of 83.2% and 76.9% of apples. The study outcome comparing fair trade via two triple histograms specified the prevailing deviations between –82% and 80% around farm gate apple prices in 47 exporting countries and the same between –83% and 83% in 46 importing countries. Based on the Gini coefficient, Ratio 20/20, and Hoover index, the accomplished evaluations quantified total disparity in apple trading by 13% to 40%, calculated misbalance between 20% of the top and bottom world traders, and grounded preferable market alignments ranged from 9% to 38%.

This paper analyzed the factors affecting consumers’ purchase intentions and decisions while purchasing wine. The study was performed based on the primary data collected with the help of a survey and a structured questionnaire using convenience sampling. A total of 120 respondents above 21 years old across India who were wine consumers and likely to purchase wine were the study population. Exploratory factor analysis, confirmatory factor analysis, descriptive analysis, and cluster analysis were the main methods used to analyze the data. The information gathered was subjected to further quantitative analysis using SPSS. Kaiser-Meyer-Olkin Measure (KMO) of sampling adequacy was 0.759, and Cronbach’s alpha was 0.817 indicating high reliability of the study. Factor analysis brought out six essential factors affecting the wine purchase decisions of Indian consumers. They are as follows: quality concerns, consumption preferences, consumption deterrents, consumption reasons, social factors, and risk factors. Furthermore, the study found that the purchase intentions of Indian wine consumers are affected by the attitude and awareness of consumers. The cluster analysis further helped to divide the Indian wine market into three segments, i.e., regular consumers comprising 44.2%, non-consumers comprising 29.2%, and occasional consumers comprising 26.7%. A few of the key factors influencing wine purchase are attributes and knowledge of the wine ingredients. In addition, friends and family play an important role in wine purchasing decisions.

The new modality caused by COVID-19 has significantly affected tourism, from the decrease in demand and the deterioration of facilities due to lack of budget to the point of becoming unattractive. In response to this, the objective of this research work is to design and validate a scale to measure Brand Personality in Tourist Destinations (BPTD). The results were structured in three phases. In the first phase, an exhaustive search of the bibliography and first version of the instrument were generated. Moreover, a panel of 12 professionals between academics and managers was held, who evaluated the features and content validation by a panel of 7 experts. In the second phase, data collection was carried out with the participation of 998 tourists. In the third phase, exploratory and confirmatory factor analysis was carried out, obtaining a KMO of 0.979 and a p-value of 0.000. The total explained variance of the 21 items grouped in three dimensions represents 87.27%. In the confirmatory analysis, χ2 = 921.57, p = 0.000, RMR = 0.069, GFI = 0.917, AGFI = 0.829, CFI = 0.980, TLI = 0.976, NFI = 0.975, IFI = 0.980 and RMSA = 0.065, with an appropriate internal consistency (performance: α = 0.984; social innovation α = 0.982; honesty α = 0.964). Composite reliability was CR > 0.70 and convergent validity was AVE > 0.5. In conclusion, the values imply an acceptable adjustment, for which the model is adequate, and the scale is valid to be applied in tourism.