E-word of mouth in sales volume forecasting: Toyota Camry case study

The Covid-19 pandemic has sped up the need for digitalization in doing daily activities including banking. Thus, banks is now in competition to give the best digital platform to serve their customers. One of the most effective form of promotion to introduce a new services is word of mouth. This study aims to investigate the effect of E-word of mouth (E-WOM) on customer decisions to choose an Islamic Bank: A Case Study of Indonesia’s Islamic Digital Banks. This research includes survey research by taking samples and using questionnaires as the main data. The study targetted 130 customers who have active accounts in the form of savings or time deposits. Primary data processing by factor analysis by testing the research model and the hypothesis of the relationship between variables, this processing uses smart PLS statistical software with the Structural Equation Modeling method with Partial Least Squares. The results of the analysis show that religious motivation and service quality have a positive effect on consumer ratings, where consumer ratings and service quality have a positive effect on E-word of mouth while convenience has no positive effect on consumer ratings and E-word of mouth on respondents. The current study contributes novelty to adjust the existing model with Islamic Digital Bank (IDB).

This study aims to determine the effect of Brand Image, E-Word of Mouth, Impulse Buying and Promotion on Purchasing Decisions at Tiktok Shop (Case Study on Students of the Mandala Jember Institute of Technology and Science) both partially and simultaneously. This type of research is quantitative. The population in this study were students of the Mandala Jember Institute of Technology and Science. The sample used was 100 respondents using the Ferdinand formula. Data analysis techniques using the Classical Assumption Test, Multiple linear regression analysis, Determination Coefficient Test (R2) and Hypothesis Test. The results showed that the Brand Image and Promotion variables partially influenced purchasing decisions at Tiktok Shop, but E-Word of Mouth and Impulse Buying had no partial effect on purchasing decisions at Tiktok Shop.

Purpose Anchoring on the social exchange theory (SET), this study aims to examine the impact of artificial intelligence (AI) on customer behavior within the e-commerce sector. This study investigates the interconnections between perceived benefits of AI, customer trust, customer satisfaction and electronic word of mouth (eWOM). Design/methodology/approach A mixed-methods approach was used in the Vietnamese e-commerce context. A quantitative survey of 291 respondents was conducted to examine the proposed relationships, while qualitative research involving semistructured interviews with 10 participants provided deeper insights. Thematic analysis of the interview data enriched and validated the quantitative findings, offering a holistic understanding of the phenomena. Findings The quantitative analysis indicates that perceived benefits of AI do not directly affect eWOM; however, they significantly influence eWOM indirectly via trust and customer satisfaction, which serve as full mediators. The qualitative findings reveal three primary themes: the impact of AI’s perceived advantages on trust and satisfaction, the indirect connection between AI’s benefits and eWOM and the mediating functions of trust and satisfaction within this framework. Originality/value This research addresses a gap in existing literature by analyzing the influence of AI on consumer behavior in a developing market, specifically focusing on Vietnam as a case study. The integration of quantitative and qualitative methods provides a comprehensive framework for analyzing AI-mediated customer interactions. The findings contribute to the theoretical framework of SET within the realm of AI, while also offering practical insights for businesses and policymakers. They emphasize strategies aimed at improving customer trust, satisfaction and eWOM in the context of e-commerce through the application of AI.

The development and progress of information technology has now penetrated the field of transportation to facilitate human activities. With the increasing need for fast and easy transportation, online transportation service businesses using applications have emerged. The use of technology in the transportation sector provides convenience in ordering, time and cost efficiency, making it a distinct advantage for online transportation service providers compared to conventional transportation. InDrive is a ridesharing application platform (online transportation service provider) which is similar to Grab and Gojek, this application allows users to order cars/motorbikes at prices that can be negotiated between the driver and passenger. This research was conducted to determine and measure the influence of E-Word of Mouth and E-Commerce on the Decision to Choose the Indrive Online Transportation Application with a case study of Marindal I, Patumbak I District. This research uses a quantitative approach by determining a judgment sampling sample with criteria where the customer is age productive work using the indrive application which was used as a research sample. Using the Slovin sampling technique with a confidence level of 90% and an error rate of 10%, the sample used in this research was 100 people. Proving the hypothesis in this research is assisted by using the SPSS version 25 application. The results of this research are that the E-Word of Mouth variable partially has a positive and significant effect on the decision to choose Indrive online transportation. Partially, the E-Commerce variable has a positive and significant effect on the decision to choose Indrive online transportation. Simultaneously, E-Word of Mouth and E-Commerce have a positive and significant influence on the decision to choose Indrive online transportation. And 60.5% of voting decisions are influenced by E-Word of Mouth and E-Commerce.

This article examines the impact of the use of photographs in online marketing for tourism through a case study based on Japanese-style inns. Nowadays, most Japanese-style inns present photographs of what they think are their key appealing elements on their own social networking and/or video/photo-sharing websites, while guests upload their photographs and write comments on travel or social networking websites and/or on the websites of e-travel agents. Through the medium of ‘netnography’, this research has identified that the photographs presented online by Japanese-style inns can affect decision-making processes of guests and/or expectations in both a positive and a negative way, and e-word of mouth can work together with the photographs to influence prospective guests.

The paper analyzes the used car market in Ukraine for the period 2017-2023. The author provides information on the volumes and structure of the used car market, analyzes the number of used cars sold from primary imports and re-registrations. It was found that the secondary car market has shown a rapid growth in the period from 2017 to 2023. In 2022, 69.37% more used cars were sold than in 2017. The share of imported cars in the used car market increased from 9.82% in 2017 to 37.75% in 2022. At the same time, the share of used passenger cars with re-registrations decreased from 90.18% in 2017 to 62.25% in 2022. The author collected statistics for the TOP-20 brands of used passenger cars according to the data of the websites "PLANETAVTO", "AUTO.RIA" and «RST» and analyzed 620 models of passenger cars by their sales volumes from May 2021 to May 2023. The paper calculated the growth rates of sales of the most popular used passenger cars in 2022-2021 and 2023-2022. According to the results of the study, the leaders of the Ukrainian used passenger car market were Volkswagen, Renault, VAZ and Skoda. The author studied the most popular models of used passenger cars, including Volkswagen Passat, Skoda Octavia, Volkswagen Golf, Ford Focus and Renault Megane, among others. It was found that the most popular types of bodywork for used passenger cars are sedans, hatchbacks and crossovers. Among the different types of passenger car classes, the most popular are mid-range and comfort class cars. The author pays special attention to environmentally friendly used passenger cars. The number of electric cars and hybrid models sold in 2021-2023 is 98,794 units, which is 7.81% of the total volume of sales of used passenger cars. The most popular models of used electric cars are Nissan Leaf, Volkswagen E-Golf, Chevrolet Bolt EV, and BMW i3. The most popular used hybrids are Toyota Camry, Hyundai Tucson, Toyota Corolla, and Nissan Qashqai. The author analyzed the factors that influence the development of the used passenger car market, including: changes in the economy, rising fuel prices, the introduction of new technologies in the automotive industry, the availability of loans and interest rates, the growing awareness of ecology and environmental standards, changes in legal norms and political decisions related to the automotive industry.

Due to short product life cycle (PLC) in the notebook computer industry, manufacturers or branders need to prepare enough quantity of spare parts for repairs or replacements after the PLC, which is the final order. But very often, the inventory of spare parts being either not enough or too much will cause various kinds of losses. Meanwhile, in these days, longer product warranty length can attract more people to buy products, but it will lead to more efforts to keep more quantities of spare parts for manufacturers or branders. In addition, the sale price of product is also an important factor to affect sales volume. Therefore, there is a trade-off phenomenon between warranty and price for sales volume. This study develops a mathematical model that includes two steps; one is to develop a profit optimization model that considers the trade-off phenomenon in order to find the best warranty, price and sales volume in the product life cycle; the other is to calculate the final order after PLC based on sales volume within the PLC and warranty length. This study uses notebook computer as an example to develop the above model. We believe that the model can serve as a planning tool to prepare inventory for the final order of products with different sales volume and warranty.

Pricing determination significantly impacts sales. Higher prices usually indicate better product quality or a premium image, targeting the middle-class market segment. However, sales volume often decreases, as consumers who purchase high-priced products tend to be more selective and value-orientated, focusing on quality or social status. Conversely, lower prices make products more accessible to a larger number of consumers, particularly those who are price-sensitive. This typically results in an increase in sales volume due to greater affordability. This research aims to explain pricing strategies and their impact on sales volume, using the online store As Shirt or Pusat Grosir Kemeja as a case study. The research adopts a qualitative approach with a descriptive method. We conduct data collection using structured interviews employing a voice recorder as the research instrument to capture the explanations from the informants. The pricing strategy used by As Shirt or Pusat Grosir Kemeja is dynamic pricing. Several factors are considered in setting their prices, including production costs, competitor pricing, and demand factors. Moreever, this dynamic pricing strategy affects the demand for As Shirt or Pusat Grosir Kemeja products, the types or products offered by As Shirt or Pusat Grosir Kemeja, and the sales volume of As Shirt or Pusat Grosir Kemeja.

This research purpose to describe the implementation of the marketing mix by consumers. To analyze the influence factors of the marketing mix to sales volume and analyze trend of sales volume of herbal medicine for the twelve months to come. The method used is a case study. Data varieties used are primary and secondary data. Source of data obtained from the PT. Dr. Sardjito Traditional Medicine Company, respondents and libraries. Methods of data collection by interview, observation, documentation and questionnaires. To describe the implementation of the marketing mix by consumers using the analytical description. To determine the influence of factors of the marketing mix to sales volume herbal medicine used multiple linear regression analysis. To analyze the trend of herbal medicine sales volume for the twelve months to come using the method of least squares trend. Result of this research show that, description of the implementation of the marketing mix according to the consumer at the high category is equal to 75,31%. Marketing mix affect to sales volume of herbal medicine significantly. Trend of sales volume of herbal medicine for the next twelve months will be decreased. Keywords: Marketing Mix, Sales Volume, Sales Trend, Herbal Medicine

PurposeThe purpose of this paper is to investigate how the use of flexible budgets may influence different institutional logics (organizational inertia and flexibility).Design/methodology/approachA qualitative research based on a single case study in a multinational subsidiary company was carried out. The data were mainly collected using the dialog technique through open-ended and semi-structured interviews and complemented with direct observation in informal and formal meetings and the analysis of internal documents. Content analysis was used for the analysis of the findings.FindingsThe use of flexible budgets, which isolates the negative variations due to the decrease in sales volume, may contribute to organizational inertia. However, this can be counterbalanced if the managers try to minimize the decline in performance through initiatives that promote organizational flexibility. In this case study, it was found that the alignment between the production director and the controller, who frequently work under different institutional logics, was important to stimulate organizational flexibility particularly in continuous improvement projects.Research limitations/implicationsThe findings of this paper are based on only one in-depth case study. Hence, the results cannot be generalized, but a theoretical contribution can be made. Furthermore, the findings are constrained by the constructs used and the specific managerial and theoretical perspectives that have supported the analysis.Practical implicationsThese results can be useful particularly for companies that are dealing with the abrupt drop in the sales volume and use the flexible budget as a performance assessment technique. These firms must pay attention because this combination can stimulate organizational inertia. To counteract this problem, it is necessary that controllers and the managers work by understanding the initiatives that promote organizational flexibility, mainly by Kaizen projects, which can minimize performance decline.Social implicationsThe main contribution may be how to deal with the different managers’ behaviors, given the decrease in sales volume, and it can help an organization survives in times of economic recession and fierce competition environments.Originality/valueThis paper contributes to both practical and academic dimensions. Indeed, despite being widely used, flexible budgeting is not a widely researched topic.

The prediction of sales volume for agricultural perishable goods is crucial for small-scale merchants to formulate corresponding procurement strategies. Accurate sales volume forecasting helps merchants reduce the risk of excess inventory, establish prudent and appropriate procurement strategies, and provide suggestions for product pricing. To further accurately explore the sales volume patterns of various perishable goods, this study classifies existing data into individual products using the STL (Seasonal-Trend decomposition using Loess) algorithm, revealing the characteristics of each product's sales volume over time. Based on the existing sales volume data, the ARIMA model is used to predict the sales volume of specific categories of goods. A multivariate linear regression model related to pricing, based on existing cost and pricing data, is established to provide merchants with pricing suggestions.

The literature survey on sales forecasting reveals that few works have been reported on long-range forecasting of sale volumes. On the other hand, there is a need of such long-range forecasting of sales data to devise suitable organisational strategy. The existing soft computing-based forecasting models provide poor prediction performance. Keeping this in view a new soft computing model is developed and utilised for prediction of seasonally adjusted (SA) and non-seasonally adjusted (NSA) sales volumes up to 24 months. The simulation results of real-life data show an excellent prediction performance compared to that of four other contemporary soft computing models.

Background. The explosion of big data (BD), automation, and machine learning have allowed contemporary businesses to better understand and predict human behavior. In scientific research big data have been widely used to study consum­er journey and opinions. One of the tools enabling forecasting of sales volume is the Bass diffusion model, which universal nature has been proven in many appli­cations in forecasting the sale of products belonging to various market segments. This article considers the use of BD as exogenous variables in the Bass model to predict the sales of tourist packages. Research aims. The purpose of the research is to assess the impact of using big data on improving the accuracy of forecasts for the sale of tourist packages. The Generalized Bass Model (GBM) has been thus expanded to include big data, which means that exogenous variables include: (1) marketer-generated content (MGC) and (2) user-generated content (UGC), including volume of web search and blog posts. Methodology. This article analyzes online news, blog posts and web search traf­fic volume related to tourist packages, and then integrates the information into the Bass model, treating it as part of the exogenous variables representing the mar­keting efforts of tour operators. It has been assumed that the volume of tour opera­tors’ web news is a proxy for content generated by marketers (MGC), while the vol­ume of blog posts and web search traffic constitute user-generated content (UGC). Key findings. The empirical analysis found that by incorporating big data into the Bass model provides more accurate prediction of tourist packages’ sales vol­ume. In addition, UGC (as an exogenous variable) is better at predicting sales volume than MGC. UGC is a fairly good tool explaining the level of interest and involvement of potential tourists. However, it has been shown that forecasting efficiency is different for blog posts and web search traffic volumes.

The financial and economic activities of economic entities in modern terms require constant plans comparison with real developments. Taking into account the peculiarities of the economic situation in the country, comparing the supply and demand, analysis of financial results, etc. The development of market relations increases the responsibility and independence of enterprises in making decisions about strategy and tactics for the future. That is why, while taking substantiating management decisions the application of techniques and methods of economic and mathematical modeling is relevant. One of the types of communication between the producer and the consumer is sales volume. It directly affects the amount of costs, profits and profitability of the enterprise. Therefore, the analysis and forecasting of such an indicator is important to increase the competitiveness of economic entities. Statistical data of one of the agricultural enterprises of Poltava region are analyzed, several influential factors are highlighted. It is proposed to perform a regression analysis, develop a linear multifactor model and build an adaptive model for forecasting the volume of sales of agricultural enterprises to select the optimal model for forecasting the studied indicator for the future. The author performed calculations on the obtained models. The analysis of the obtained results confirmed model’s adequacy, so they can be used for forecasting. As a result of comparison forecasts of sales volumes at the enterprise the adaptive model is recommended for practical application as it has the best quality of the forecast and the minimum sum of deviations squares. This model giving more weight to the latest statistics provides an optimistic forecast for the future. Thus, developing economic and mathematical forecasting models will provide an opportunity to improve the operational planning of production the certain types of products, as well as allow (after receiving new statistics) to quickly change the production monthly plans. Thus, conditions will be created to improve the quality of management, increase competitiveness and ensure the stabilization of the financial state at agricultural enterprises.

The article deals with solving the problem of forecasting sales volume on marketplaces based on the use of time series analysis methods and tools. It presents a detailed analysis of time series analysis methods and tools in the context of sales volume forecasting. and considers various approaches to time series analysis, including statistical methods, machine learning and deep learning. The article also describes in detail the use of methods such as ARIMA, GARCH to predict sales volume on marketplaces. The features of these marketplaces and their impact on the choice of time series analysis methods are discussed. Special attention is paid to the selection of features and assessment of the quality of forecasting models. The article reports the results of experiments conducted on real marketplace data, as well as a comparison of various time series analysis methods in the context of sales volume forecasting. Summarizing, the authors draw conclusions about the advantages and disadvantages of various approaches and offer recommendations on the choice of time series analysis methods to solve the problem of forecasting sales volume on marketplaces. In the context of marketplaces, multiple regression is considered to analyze and predict various economic indicators related to their activities. The relationship between sales volume, product prices, advertising costs and other factors affecting profitability is studied. The main factors influencing the success of a business on an electronic platform are identified and which of them are of the greatest importance. When analyzing data on marketing activities, product prices and customer loyalty, using multiple regression, it is possible to determine which factors most strongly affect profitability and, based on the data obtained, develop effective strategies to increase its yield. When analyzing data from the marketplace, there may be external variables, such as economic conditions or changes in legislation, that may affect profitability, but are not directly related to the independent variables under consideration. Time series analysis methods and tools allow for those factors to be taken into account and thus to determine the true impact of independent variables on dependent variables.