BRAND TRANSFER BY HANDPHONE CONSUMERS IN THE CITY OF JUANG BIREUEN

Various studies that have been carried out in the City of Juang Bireuen to see how far mobile phone consumers have switched to other brands, the findings or results of this study show a very significant impact on the switching of mobile phone brands, so that mobile phone manufacturers are charged with the desires that are of interest to consumers. The results carried out in the City of Juang Bireuen with respondents who had changed to another cellphone brand at least 1 time with 100 respondents the sample population showed that all averages were influenced by advertisements, price changes, and dissatisfaction in a cellphone brand, causing brand switching. The results of this study indicate that advertising has a positive and significant effect on the transfer of mobile phone brands. Likewise, price changes have a positive and significant effect on the transfer of mobile phone brands. Both with dissatisfaction have a positive effect on switching brands of mobile phones. Price changes strengthen the relationship between advertising and brand switching with interaction, meaning that price changes are purely an interaction variable. Likewise, price changes strengthen the relationship between dissatisfaction with brand switching and interaction. Based on the results of this study, there are several recommendations for consumers and mobile phone manufacturers, with increasing consumer interest in changing cellphone brands, it is an important task for manufacturers to be able to satisfy consumer desires

Did the COVID-19 Pandemic Crisis Affect Housing Prices Evenly in the U.S.?

While it is well-known that housing prices generally increased in the United States (U.S.) during the COVID-19 pandemic crisis, to the best of our knowledge, there has been no research conducted to understand the spatial patterns and heterogeneity of housing price changes in the U.S. real estate market during the crisis. There has been less attention on the consequences of this pandemic, in terms of the spatial distribution of housing price changes in the U.S. The objective of this study was to explore the spatial patterns and heterogeneous distribution of housing price change rates across different areas of the U.S. real estate market during the COVID-19 pandemic. We calculated the global Moran’s I, Anselin’s local Moran’s I, and Getis-Ord’s statistics of the housing price change rates in 2856 U.S. counties. The following two major findings were obtained: (1) The influence of the COVID-19 pandemic crisis on housing price change varied across space in the U.S. The patterns not only differed from metropolitan areas to rural areas, but also varied from one metropolitan area to another. (2) It seems that COVID-19 made Americans more cautious about buying property in densely populated urban downtowns that had higher levels of virus infection; therefore, it was found that during the COVID-19 pandemic year of 2020–2021, the housing price hot spots were typically located in more affordable suburbs, smaller cities, and areas away from high-cost, high-density urban downtowns. This study may be helpful for understanding the relationship between the COVID-19 pandemic and the real estate market, as well as human behaviors in response to the pandemic.

Principles of Multitrading

Modern software and hardware tools provide unprecedented freedom for a variety of activities in the forex markets, from trading to analyzing the feasibility of models of nonlinear processes in self-organizing systems. To reduce risks and increase the efficiency of interaction with stock market instruments, it is proposed to provide variable adaptability of trading by combining trading strategies using several trading accounts of different brokers, multiple financial instruments, and Complex Indicators Tendencies of price changes. As a result of three years of experimental work, the basic principles of multitrading have been formulated and tested, and an information environment has been compiled, contributing to the development of an individualized trading system. The basic concept of organizing a multitrading information environment: the use of specialized hardware and software systems for strategic analysis and forecasting of price changes for an individual financial instrument, tactical selection of a promising financial instrument from the available set, and effective operating activities with orders of trading accounts. It can be expected that the evolution of the principles of multitrading will lead to the creation of analytical systems for predicting the kinetics of non-equilibrium changes in the characteristic parameters of self-organizing cooperative systems for wide application in biology, cybernetics, economics, and the social sphere.

EXPRESS: The Impact of Historical Price Information on Purchase Deferral

In this paper, the authors examine how historical price information influences consumers’ decision to defer a purchase. They focus on two aspects of historical price information: the direction and the frequency of past price changes. The authors advance a theoretical framework which postulates that the interaction between these two factor shapes consumers’ decisions to buy now versus later. Controlling for the total magnitude of price changes, the authors propose that consumers are more likely to defer purchase when the price of the product has previously increased compared to when the price has decreased. Importantly, the authors hypothesize that this effect is more pronounced when consumers observe a single large change in price (e.g., an increase of $100 versus a decrease of $100) compared to when they observe multiple smaller changes that establish a trend (e.g., four decreases of $25 versus four increases of $25). The authors argue that these effects are driven by differences in consumers’ expectations about future prices. They test their predictions, as well as two moderators of the proposed effects—the monotonicity and the timing of price changes—in six well-powered pre-registered experimental studies (N = 5,713) using both hypothetical and actual purchases.