Sara Salimi; Ali Hajiha; Hamidreza Saeednia; Kambiz Heidarzadeh
Abstract
The purpose of this study is to design a post-purchase regret model and determine online business strategies. Regret is a state of mind in which the customer is hesitant to buy a product or service. This hesitation can be due to paying a high price for the quality received, comparing the quality of the ...
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The purpose of this study is to design a post-purchase regret model and determine online business strategies. Regret is a state of mind in which the customer is hesitant to buy a product or service. This hesitation can be due to paying a high price for the quality received, comparing the quality of the goods or services received with competing companies, or the result of various risks that may arise in online shopping. To design the regret model, the qualitative research method was used utilizing the grounded theory strategy and the Strauss-Corbin systematic design. The sampling method was judgmental and to collect information and achieve theoretical saturation, 14 semi-structured interviews were conducted with university professors and managers of online commerce and web-based businesses. The key points of the interviews were analyzed during the three stages of open, axial, and selective coding. For the validity and reliability of the research, the members` review, participatory, triangulation, and retest methods were used. The results were extracted in a paradigm model with 20 categories and 76 concepts. The Delphi method was used to prioritize the constructive factors of the model and the opinion of experts was determined in 2 stages and converged with a standard deviation of less than 0.05. The results of the research help online business activists to gain an accurate understanding of post-purchase regrets in online shopping behavior.
Golnar Adabi; Ali Hajiha; Farhad Hosseinzadeh Lotfi
Abstract
Studies in the field of the tourism industry are often carried out using classical statistics. While the use of spatial statistics in tourism helps a lot in identifying existing models and trends in the industry and discovering them. Due to the interaction between economic, political, environmental and ...
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Studies in the field of the tourism industry are often carried out using classical statistics. While the use of spatial statistics in tourism helps a lot in identifying existing models and trends in the industry and discovering them. Due to the interaction between economic, political, environmental and social elements in tourism activities, analysis methods of spatial statistics can be used by identifying information between samples and using large volumes of information by not indicating the independence of the data, can obtain suitable tourism clusters and help identify the appropriate tourism model in Iran. This study aims at to design a model of the tourism industry in Iran with the approach of the spatial correlation structure. The research method was qualitative and quantitative. To identify the variables affecting the tourism industry, the qualitative meta-analysis method, and to collect the required data in spatial statistics, the data of the Cultural Heritage and Tourism Organization in the summer of 2008-2018 have been used. To determine the model of tourism clusters Moran statistics and to study tourism clusters in all provinces of the country, the best interpolation method of tourism has been determined. ArcGIS software was used to analyze the research data. The results of data analysis showed that tourism data has a spatial autocorrelation and a cluster and regular model in the statistical period of summer 2008 to 2018. The most cluster model of tourism using the Moran spatial autocorrelation index is related to the summer of 2008 with 0.991 and the lowest cluster model of tourism is related to the summer of 2014 with the amount of 0.976. Also, the results of the study of the distribution of tourism direction in the provinces of the country in this statistical period showed that the predominant direction of tourism is with a slight change from northwest to southeast.