Journal articles on the topic 'Consumer behavior Forecasting'
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RATNER, Svetlana V., and Artem M. SHAPOSHNIKOV. "Forecasting changes in consumer behavior in conditions of economic crisis." Economic Analysis: Theory and Practice 21, no. 5 (May 30, 2022): 911–26. http://dx.doi.org/10.24891/ea.21.5.911.
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Subject. The article addresses the issue of forecasting changes in consumer behavior patterns in the medium and long term. Objectives. The focus is on consumer behavior prediction to identify potential growth points for small and medium-sized businesses in the field of trade and services. Methods. The methodological basis of the study is behavioral economics. The information base draws on analytical reports of Cerulli Associates, Profi Online Research, PricewaterhouseCoopers, Euromonitor International, and Ipsos research company, which developed and uses in its analysis a calculation methodology for the Global Consumer Confidence Index. Results. We analyzed the main trends of consumer behavior in 2022 in Russia in the context of tougher sanctions, global supply disruptions, and other crisis phenomena, from the perspective of existing knowledge about sustainable forms of Russian consumers’ reaction to crisis events in the economy. The paper highlights a decrease in loyalty to brands, expansion of online commerce to people over the age of 60, reduced demand for eco-products, and enhanced financial literacy, which is accompanied by an increase in demands for a fair price-quality ratio, as the key changes in consumer behavior that determine opportunities for businesses. Conclusions. Crises seriously affect the time frames of consumer planning. During the crisis, the time frames are reduced and blurred, and now consumers proceed from even greater uncertainty.
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Kim, Dayoon, Jin Won Mun, Daniel Jin Won Kim, and Soo Hyun Ahn. "Market Predictor: Game Theory Model Forecasting Consumer Choice through Analysis of Simultaneous Marketing Strategies and Consumer Behavior." International Journal of Trade, Economics and Finance 8, no. 3 (June 2017): 165–68. http://dx.doi.org/10.18178/ijtef.2017.8.3.556.
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Hora, Cristina, Florin Ciprian Dan, Gabriel Bendea, and Calin Secui. "Residential Short-Term Load Forecasting during Atypical Consumption Behavior." Energies 15, no. 1 (January 1, 2022): 291. http://dx.doi.org/10.3390/en15010291.
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Short-term load forecasting (STLF) is a fundamental tool for power networks’ proper functionality. As large consumers need to provide their own STLF, the residential consumers are the ones that need to be monitored and forecasted by the power network. There is a huge bibliography on all types of residential load forecast in which researchers have struggled to reach smaller forecasting errors. Regarding atypical consumption, we could see few titles before the coronavirus pandemic (COVID-19) restrictions, and afterwards all titles referred to the case of COVID-19. The purpose of this study was to identify, among the most used STLF methods—linear regression (LR), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN)—the one that had the best response in atypical consumption behavior and to state the best action to be taken during atypical consumption behavior on the residential side. The original contribution of this paper regards the forecasting of loads that do not have reference historic data. As the most recent available scenario, we evaluated our forecast with respect to the database of consumption behavior altered by different COVID-19 pandemic restrictions and the cause and effect of the factors influencing residential consumption, both in urban and rural areas. To estimate and validate the results of the forecasts, multiyear hourly residential consumption databases were used. The main findings were related to the huge forecasting errors that were generated, three times higher, if the forecasting algorithm was not set up for atypical consumption. Among the forecasting algorithms deployed, the best results were generated by ANN, followed by ARIMA and LR. We concluded that the forecasting methods deployed retained their hierarchy and accuracy in forecasting error during atypical consumer behavior, similar to forecasting in normal conditions, if a trigger/alarm mechanism was in place and there was sufficient time to adapt/deploy the forecasting algorithm. All results are meant to be used as best practices during power load uncertainty and atypical consumption behavior.
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K, Nimala, and Thamizh Arasan. R. "Energy Analytics for Smart Meter Data using Consumer Centric Approach." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 656. http://dx.doi.org/10.14419/ijet.v7i3.12.16448.
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A short-range residential consumer’s demand forecasting at the distinct and cumulative level, by an analysis of data using consumer based centric approach. Energy intake behavior might fluctuate among various seasonal factors; the consumed current will change from one season to other. So hereby we are building a model which helps to calculate future electricity consumption data from the obtain ability of past smart meter data. Currently utility companies accumulate the data, use it, share for further practice, and abandon usage data at their discretion, with no input from customers. In many cases, consumers do not even have entree to their own data. But in this project Consumer can have fast admittance and control over their individual data, and also helps to choose the familiar algorithms for the data analyze rather than including third party applications. By end of analyze technique, the analyzed output will be driven to some user interactive application by creating a Graphical User Interface.
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Shumilo, Yana. "Technology for modeling the mechanism of reflective control of herd behavior of consumers in the sales markets." Management of Economy: Theory and Practice. Chumachenko’s Annals, no. 2019 (2019): 237–48. http://dx.doi.org/10.37405/2221-1187.2019.237-248.
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The problem of controlling the herd behavior of consumers in the product sales markets has been identified. A general outline of the mechanism for reflective management of the decision-making process on the purchase of goods and the manifestation of herd behavior by consumers in the sales markets was presented. The stages of the technology for constructing a model of the mechanism of reflective control of herd behavior of consumers in the sales market have been described and formalized. The possibility of using the model as a tool for forecasting and increasing demand for a particular product or group of products has been determined. Promising areas of research have been identified. Keywords herd behavior, consumer, reflexive control, product sales market, decision making.
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Shinkarenko, Volodymyr, Alexey Hostryk, Larysa Shynkarenko, and Leonid Dolinskyi. "A forecasting the consumer price index using time series model." SHS Web of Conferences 107 (2021): 10002. http://dx.doi.org/10.1051/shsconf/202110710002.
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This article examines the behavior of the consumer price index in Ukraine for the period from January 2010 to September 2020. The characteristics of the initial time series, the analysis of autocorrelation functions made it possible to reveal the tendency of their development and the presence of annual seasonality. To model the behavior of the consumer price index and forecast for the next months, two types of models were used: the additive ARIMA*ARIMAS model, better known as the model of Box-Jenkins and the exponential smoothing model with the seasonality estimate of Holt-Winters. As a result of using the STATISTICA package, the most adequate models were built, reflecting the monthly dynamics of the consumer price index in Ukraine. The inflation forecast was carried out on the basis of the Holt-Winters model, which has a minimum error.
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Khan, Anam-Nawaz, Naeem Iqbal, Atif Rizwan, Rashid Ahmad, and Do-Hyeun Kim. "An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings." Energies 14, no. 11 (May 23, 2021): 3020. http://dx.doi.org/10.3390/en14113020.
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Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting.
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Herbig, Paul, John Milewicz, and James E. Golden. "Differences in Forecasting Behavior between Industrial Product Firms and Consumer Product Firms." Journal of Business & Industrial Marketing 9, no. 1 (March 1994): 60–69. http://dx.doi.org/10.1108/08858629410053498.
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Kok, Ali, Ergün Yükseltan, Mustafa Hekimoğlu, Esra Agca Aktunc, Ahmet Yücekaya, and Ayşe Bilge. "Forecasting Hourly Electricity Demand Under COVID-19 Restrictions." International Journal of Energy Economics and Policy 12, no. 1 (January 19, 2022): 73–85. http://dx.doi.org/10.32479/ijeep.11890.
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The rapid spread of the COVID-19 pandemic has severely impacted many sectors including the electricity sector. The restrictions such as lockdowns, remote-working, and -schooling significantly altered the consumers' behaviors and demand structure especially due to a large number of people working at home. Accurate demand forecasts and detailed production plans are crucial for cost-efficient generation and transmission of electricity. In this research, the restrictions and their corresponding timing are classified and mapped with the Turkish electricity demand data to analyze the impact of the restrictions on total demand using a multiple linear regression model. In addition, the model is utilized to forecast the electricity demand in pandemic conditions and to analyze how different types of restrictions impact the total electricity demand. It is found that among three levels of COVID-19 restrictions, age-specific restrictions and the complete lockdown have different effects on the electricity demand on weekends and weekdays. In general, new scheduling approaches for daily and weekly loads are required to avoid supply-demand mismatches as COVID-19 significantly changed the consumer behavior, which appears as altered daily and weekly load profiles of the country. Long-term policy implications for the energy transition and lessons learned from the COVID-19 experience are also discussed.
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Кокодей, Татьяна Александровна, and Иван Константинович Соколов. "Determining Consumer Type at Food Market." ВЕСТНИК ОБРАЗОВАНИЯ И РАЗВИТИЯ НАУКИ РОССИЙСКОЙ АКАДЕМИИ ЕСТЕСТВЕННЫХ НАУК, no. 3 (October 15, 2019): 24–26. http://dx.doi.org/10.26163/raen.2019.98.79.006.
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В маркетинговом исследовании, представленном в данной статье, выявлены основные типы потребителя на рынке продуктов питания для последующего анализа и прогноза их поведения. В зависимости от доминирующих мотивов и возможностей потребления выделены пять основных типов потребителей продуктов питания: «Сдержанный», «Безразличный», «Органический», «Социальный или VIP» и «Активный». We distinguish the main types of consumers in the food market for further analysis and forecasting of their behavior. Depending on the dominant motives and consumption potential five main types of food consumers are studied, namely, “Discreet”, “Indifferent”, “Organic”, “Social or VIP” and “Active”.
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AbuBaker, Maher. "Data Mining Applications in Understanding Electricity Consumers’ Behavior: A Case Study of Tulkarm District, Palestine." Energies 12, no. 22 (November 11, 2019): 4287. http://dx.doi.org/10.3390/en12224287.
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This paper presents a comprehensive data analysis and visualization of electricity consumers’ prepaid bills of Tulkarm district. We analyzed 250,000 electricity consumers’ prepaid bills covering the time period from June to December 2018. The application of data mining techniques for understanding electricity consumers’ behavior in electricity consumption and their behavior in charging their electricity meter’s smart cards in terms of quantities charged and charging frequencies in different time periods, areas and tariffs are used. Understanding consumers’ behavior will support planning and decision making at strategic, tactical and operational levels. This analysis is useful for predicting and forecasting future demand with a certain degree of accuracy. Monthly, weekly, daily and hourly time periods are covered in the analysis. Outliers detection using visualization tools such as box plot is applied. K-means unsupervised machine learning clustering algorithm is implemented. The support vector machine classification method is applied. As a result of this study, electricity consumers’ behavior in different areas, tariffs and timing periods is understood and presented by numbers and graphs and new electricity consumer segmentation is proposed.
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Hrustek, Nikolina Žajdela, Damira Keček, and Antonio Gazdek. "Development and demonstration of software application functionality for solving the problem of consumer behavior prediction." Croatian Regional Development Journal 3, no. 1 (June 1, 2022): 75–90. http://dx.doi.org/10.2478/crdj-2022-0005.
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Abstract Markov chains have a wide application in predicting the movement of various phenomena. The aim of this paper is to present the development and functionality of a software application for solving the problem of predicting consumer behavior using the Markov chain method. The software application is primarily intended for predicting the use of electronic services and forecasting the number of users. The work of the software application was tested on the example of an organization whose activity is the provision of electronic and financial services. The importance of predicting the use of services and forecasting the number of users is reflected in the assessment of the capacity of servers on which the services of the organization are located. The software application contains user instructions for easier use and interpretation of the obtained results.
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Shulhina, Liudmyla. "Methodology for Forecasting Revenues from the Sale of Innovative Products in the Domestic Tourism Market." Marketing of Scientific and Research Organizations 41, no. 3 (September 1, 2021): 95–114. http://dx.doi.org/10.2478/minib-2021-0016.
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Abstract This article presents a methodology for forecasting the expected sales of innovative tourism products in the domestic market. The principles of the product life cycle concept and consumer behavior theory are taken as starting points for calculating the sales volumes of an innovative product as well as the rate of its penetration into the market. A method of measuring the level of consumer commitment to a travel agency and its offerings is posited, and the relationship between the structure of the target market and market activity in purchasing tourist products is demonstrated. Deep market segmentation is applied to take into account the behavioral peculiarities of individual subsegments (Loyalists Market, Sympathizers Market, Qualified Market, Finders Market, Serviced Market, Possible Market, Potential Consumers Market, Perspective Market). Formulas are proposed for calculating the volume of each of the identified markets. An improved and adapted model for the tourist market (by E. Rogers and F. Bass) is used to calculate the diffusion rates of domestic tourist products. This methodology of forecasting the expected sales of innovative tourism products in the domestic market is empirically confirmed based on data on the domestic tourism market in the region of Vinnytsiya, Ukraine.
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Anwar, Andi Faisal, Idris Parakkasi, and Bahrul Ulum Rusydi. "Tinjauan Sosiologi Ekonomi Terhadap Perilaku Konsumsi Masyarakat Kota Makassar Pada Pasar Virtual." AL-FALAH : Journal of Islamic Economics 3, no. 1 (July 17, 2018): 93. http://dx.doi.org/10.29240/jie.v3i1.346.
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This study aims to analyze the behavior of public consumption, the factors that encourage people to transact in the virtual market, and forecasting the type of the market in the future related by consumption behavior. Therefore, the analytical method used is descriptive statistical analysis. The results showed that in terms of characteristics of people who spend in the virtual market dominated by women, high school educated, and having aged between 21-30 years, sources of information through social media, and the type of goods is a fashion product. In terms of consumer behavior and the value of goods, generally consumers still doubt on the quality of goods sold in the virtual market. The reasons of shoppings are efficiency and effectiveness as well as availability of new products. Furthermore, in terms of consumption behavior and hypereality, people tend to browse despite the desire to buy goods quite low, consumer exposure in non-virtual market will decrease in stead of in the virtual market, and then some people still doubt the level of trust and security transact in the virtual market. Religious values are essential to improving people's morale in exploiting virtual markets
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Yi, Xin. "Construction of Online Agricultural Product Consumption Demand Analysis Model Based on Data Mining Technology." Advances in Multimedia 2022 (February 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/8229484.
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The traditional offline marketing mode of agricultural products was relatively single and difficult to adapt to the effective communication between agricultural and commercial enterprises and customers under the new situation, while the coordination between online agricultural products marketing management and customers’ consumption needs was not enough. Therefore, this paper puts forward the research on the construction of online agricultural product consumption demand analysis model based on data mining technology. Based on the supply-demand relationship between agricultural and commercial enterprises and consumers, this paper analyzed consumers and their demand behavior for agricultural products and gave an evaluation model of customers’ consumption demand for agricultural products. Using data mining technology, this paper made a cluster analysis on the characteristics of consumer demand behavior and established the mapping relationship between agricultural products and consumer demand. Then, the online consumption demand prediction and analysis model of agricultural products was put forward. Finally, the performance of demand forecasting model based on data classification was analyzed. The empirical results showed that the model proposed in this paper can better predict and analyze the consumption demand behavior of online agricultural products and had certain feasibility and effectiveness. The online consumption demand analysis model proposed in this paper played a positive role in improving the marketing management level of agricultural products.
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Tсeplit, Anna P., Antonina A. Grigoreva, and Tatyana A. Skripkina. "The Models of Supporting the Strategic Decisions on Engineering Products Competitiveness." Applied Mechanics and Materials 770 (June 2015): 656–61. http://dx.doi.org/10.4028/www.scientific.net/amm.770.656.
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The authors suggest the models of competitiveness assessment of engineering products at the early stages of the product life cycle. The criteria-based approach and the machinery of fuzzy sets theory are applied when developing the models. The model of consumers’ preferences forecasting allows calculating the prospective demand, motivation of consumer behavior, their attitude to the supplied product. The model based on the pair-wise comparison approach calculates fuzzy sets of various degrees of product competitiveness. The model of engineering product rating ensures rational selection of alternatives under the conditions of collective selection at all stages of the product life cycle.
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Levina, Alena, Yuliya Yakunina, and Tatiana Konovalova. "APPLICATION OF FORECASTING METHODS IN PROCUREMENT LOGISTICS OF TRADING ENTERPRISES." Bulletin of the South Ural State University series "Economics and Management" 16, no. 2 (2022): 165–73. http://dx.doi.org/10.14529/em220216.
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The article authors consider the possibilities of using various methods for forecasting the volume of sales, based on logistical principles, in relation to the activities of automobile dealer enterprises. The golden rule of logistics forms the basis for the logistics process behavior and is implies the supply of products in the right quantity and quality, and on time. The task of sales forecasting is to anticipate the demand parameters to plan the future activities of the enterprise. Thus, sales forecasting underlies the logistics activities of the enterprise. The effectiveness of the implementation of almost all logistics functions directly depends on the accuracy and reliability of the consumer demand forecasts. At the same time, the specifics of the activity of the enterprise in question – a car dealer – imposes certain restrictions on the possibilities of sales forecasting. In particular, the need to take into account market trends, the availability of quotas for the manufacturer, and taking into consideration the demand for the assortment on the part of consumers lead to the need to adjust the methods used for sales forecasting. The authors of the article have reviewed the forecasting methods used in logistics and highlighted the methods that can be used by car dealers to determine the volume of sales in subsequent periods. The sequence of using forecasting methods forms an algorithm for sales volume forecasting. Approbation of the methodology and algorithm for sales volume forecasting has been carried out on the example of a particular enterprise and can be used by similar enterprises in their activities.
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Maeng, Kyuho, Jihwan Kim, and Jungwoo Shin. "Demand forecasting for the 5G service market considering consumer preference and purchase delay behavior." Telematics and Informatics 47 (April 2020): 101327. http://dx.doi.org/10.1016/j.tele.2019.101327.
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Ahmed, Rizwan Raheem, Dalia Streimikiene, Zahid Ali Channar, Hassan Abbas Soomro, Justas Streimikis, and Grigorios L. Kyriakopoulos. "The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry." Sustainability 14, no. 14 (July 12, 2022): 8546. http://dx.doi.org/10.3390/su14148546.
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This research aims to examine a neural network (artificial intelligence) as an alternative model to examine the neuromarketing phenomenon. Neuromarketing is comparatively new as a technique for designing marketing strategies, especially advertising campaigns. Marketers have used a variety of different neuromarketing tools, for instance functional magnetic resonance imaging (fMRI), eye tracking, electroencephalography (EEG), steady-state probe topography (SSPT), and other expensive gadgets. Similarly, researchers have been using these devices to carry out their studies. Therefore, neuromarketing has been an expensive project for both companies and researchers. We employed 585 human responses and used the neural network (artificial intelligence) technique to examine the predictive consumer buying behavior of an effective advertisement. For this purpose, we employed two neural network applications (artificial intelligence) to examine consumer buying behavior, first taken from a 1–5 Likert scale. A second application was run to examine the predicted consumer buying behavior in light of the neuromarketing phenomenon. The findings suggest that a neural network (artificial intelligence) is a unique, cost-effective, and powerful alternative to traditional neuromarketing tools. This study has significant theoretical and practical implications for future researchers and brand managers in the service and manufacturing sectors.
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Ryu, Ga-Ae, Aziz Nasridinov, HyungChul Rah, and Kwan-Hee Yoo. "Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data." Agriculture 10, no. 1 (January 18, 2020): 21. http://dx.doi.org/10.3390/agriculture10010021.
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It is believed that the huge amount of information delivered to the consumers through mass media, including television and social networks, may affect consumers’ behavior. The purpose of this study was to forecast the amount required to purchase pork belly meat by using unstructured data such as broadcast news, TV programs/shows and social network as well as structured data such as consumer panel data, retail and wholesale prices and production outputs in order to prove that mass media data release can occur ahead of actual economic activities and consumer behavior can be predicted by using these data. By using structured and unstructured data from 2010 to 2016 and five forecasting algorithms (autoregressive exogenous model and vector error correction model for time series, gradient boosting and random forest for machine learning, and long short-term memory for recurrent neural network), the amounts required to purchase pork belly meat in 2017 were forecasted and compared with the actual amounts to validate model accuracy. Our findings suggest that when unstructured data were combined with structured data, the forecast pattern is improved. To date, our study is the first report that forecasts the demand of pork meat by using structured and unstructured data.
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Lockerbie, Brad. "After the Vote: Evaluating a Prospective Forecasting Model of Presidential Elections." PS: Political Science & Politics 38, no. 1 (January 2005): 39–40. http://dx.doi.org/10.1017/s1049096505055769.
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There are two components to my model of presidential election forecasting: Given the enormous amount of work on voting behavior that finds prospective assessments of the economy to be strongly related to vote choice (e.g., Abramowitz 1985; Kuklinski and West 1981; Lewis-Beck 1988; Lockerbie 1992), I make use of a prospective economic item from the Survey of Consumer Attitudes and Behavior that asks if the next year will be better, worse, or the same for the respondent. I take the average of the negative responses to this question from the first quarter of the election year as my economic measure. These data are available in late April.
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Silva, Ana Lucia Rodrigues da, and Reinaldo Castro Souza. "Forecasting the New Trends about the Consumer Behavior in the Cruise Industry Post COVID-19." Journal of Data Analysis and Information Processing 10, no. 01 (2022): 58–77. http://dx.doi.org/10.4236/jdaip.2022.101004.
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Kapaj, Ilir, and Ana Mane Kapaj. "An Analysis of Household Consumption of Dairy Products." Archives of Business Research 9, no. 1 (February 5, 2021): 148–53. http://dx.doi.org/10.14738/abr.91.9681.
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Household consumption of dairy products is one the major component in the total sales of the Albanian dairy industry. Therefore, understanding the factors which may significantly influence household consumption is important in the planning of farmers, processors and manufacturers. Consumers' responses to changes in price and non-price factors are basic to an economic analysis of almost all the policy decisions related to industry or government programs. Forecasting the future direction of household consumption, and how that direction might be modified through industry efforts or by national programs and policies, requires information on the relationships among prices, incomes, household characteristics and consumer demand. This study focuses on households as consuming units, explains and analyzes their purchasing behavior for dairy products. As milk is a very important component of the Albanians diet, this study explores consumer preferences for milk in Albania and also tries to determine consumers profiles based on their preferences and socio-demographic factors. To reach these objectives, this research designed a conjoint choice experiment survey and collected primary data in the most populated cities of Albania. This study provides useful information to different stakeholders including milk producers and importers. The milk industry and its marketers may benefit from this information by using it to strategically market their milk to different groups.
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Mahdy, Basma, Hazem Abbas, Hossam Hassanein, Aboelmagd Noureldin, and Hatem Abou-zeid. "A Clustering-Driven Approach to Predict the Traffic Load of Mobile Networks for the Analysis of Base Stations Deployment." Journal of Sensor and Actuator Networks 9, no. 4 (November 23, 2020): 53. http://dx.doi.org/10.3390/jsan9040053.
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Mobile network traffic is increasing in an unprecedented manner, resulting in growing demand from network operators to deploy more base stations able to serve more devices while maintaining a satisfactory level of service quality. Base stations are considered the leading energy consumer in network infrastructure; consequently, increasing the number of base stations will increase power consumption. By predicting the traffic load on base stations, network optimization techniques can be applied to decrease energy consumption. This research explores different machine learning and statistical methods capable of predicting traffic load on base stations. These methods are examined on a public dataset that provides records of traffic loads of several base stations over the span of one week. Because of the limited number of records in the dataset for each base station, different base stations are grouped while building the prediction model. Due to the different behavior of the base stations, forecasting the traffic load of multiple base stations together becomes challenging. The proposed solution involves clustering the base stations according to their behavior and forecasting the load on the base stations in each cluster individually. Clustering the time series data according to their behavior mitigates the dissimilar behavior problem of the time series when they are trained together. Our findings demonstrate that predictions based on deep recurrent neural networks perform better than other forecasting techniques.
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Kolokolchykova, Iryna, Roman Oleksenko, Nina Rybalchenko, Liudmyla Yefimenko, and Ganna Ortina. "Perceive of organic products by Ukrainian consumers and problems of shaping market demand." Revista Amazonia Investiga 10, no. 39 (May 5, 2021): 169–77. http://dx.doi.org/10.34069/ai/2021.39.03.16.
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Organic production is a decisive factor in preserving the natural wealth and fertility of soils, a source of food security. The market demand formation depends on the organic products perception and consumer preferences of potential buyers. Potential organic production research questions in Ukraine with an estimation of buyers consumption readiness of production are actual. These problens raise issues that need to be addressed. The reseafch evaluates the Ukrainian organic market development, identifies current trends in organic production with its capabilities in domestic and foreign markets, assesses the main donors of organic movement for the country, reveals the consumer demand formation for products through analysis of main motives and incentives, the organic products perception level by Ukrainian consumers and the influence strength on the demand formation. The methodological approach substantiates the selected problems of product consumption and ways to solve them through the economic and management theories applications, dialectical method, abstract method – logical analysis, critical thinking, systematization and formalization method, survey, expert assessments, method of logical analysis, forecasting. The methods application allowed assessing the organic products market and its operators, to identify segments, to model their behavior with an emphasis on strengthening consumer motivation. The research effectiveness is to find ways to strengthen the market demand formation for organic products within the country and its position in international markets, reduce production risks and address the perception of organic products.
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Kim, Dongha, JongRoul Woo, Jungwoo Shin, Jongsu Lee, and Yongdai Kim. "Can search engine data improve accuracy of demand forecasting for new products? Evidence from automotive market." Industrial Management & Data Systems 119, no. 5 (June 10, 2019): 1089–103. http://dx.doi.org/10.1108/imds-08-2018-0347.
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Purpose The purpose of this paper is to analyze the relationship between new product diffusion and consumer internet search patterns using big data and to investigate whether such data can be used in forecasting new product diffusion. Design/methodology/approach This research proposes a new product diffusion model based on the Bass diffusion model by incorporating consumer internet search behavior. Actual data from search engine queries and new vehicle sales for each vehicle class and region are used to estimate the proposed model. Statistical analyses are used to interpret the estimated results, and the prediction performance of the proposed method is compared with other methods to validate the usefulness of data for internet search engine queries in forecasting new product diffusion. Findings The estimated coefficients of the proposed model provide a clear interpretation of the relationship between new product diffusion and internet search volume. In 83.62 percent of 218 cases, analyzing the internet search pattern data are significant to explain new product diffusion and that internet search volume helps to predict new product diffusion. Therefore, marketing that seeks to increase internet search volume could positively affect vehicle sales. In addition, the demand forecasting performance of the proposed diffusion model is superior to those of other models for both long-term and short-term predictions. Research limitations/implications As search queries have only been available since 2004, comparisons with data from earlier years are not possible. The proposed model can be extended using other big data from additional sources. Originality/value This research directly demonstrates the relationship between new product diffusion and consumer internet search pattern and investigates whether internet search queries can be used to forecast new product diffusion by product type and region. Based on the estimated results, increasing internet search volume could positively affect vehicle sales across product types and regions. Because the proposed model had the best prediction power compared with the other considered models for all cases with large margins, it can be successfully utilized in forecasting demand for new products.
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Saidan, Suriati, Husna Saaidin, Wan Nadhra Ixora Wan Kamarulbaharin, Norzaleha Zainun, and Mohd Hafnidzam Adzmi. "Muslimah Design Trends Through The Role Of Fashion Forecasting." Idealogy Journal 7, no. 1 (April 1, 2022): 31–40. http://dx.doi.org/10.24191/idealogy.v7i1.331.
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Muslimah fashion design nowadays is a fashion trend that is the best alternative for Muslim women who want to cover their aurat with an attractive style. With a variety of options, Islamic clothing is not considered conservative or outdated. Therefore the form of fashion design should be more contemporary and in accordance with Islamic characteristics and suitable for use by all nations. In this paper the researcher will look at the fashion forecasting process used in the production of Muslimah clothing. Fashion trends are an important element in determining the concept of clothing design. As a trend forecasting concept, several things have a significant impact on the fashion industry. The ability to forecast trends in fashion, technology, and culture is a critical area of the marketing industry dedicated to identifying patterns of consumer behavior while helping companies and brands connect with audiences. Fashion trends are styles of clothing and accessories that are popular at a particular time. It will affect the popularity and lifestyle for example through the use of colors and fabrics used. Fashion forecasters will do research somewhere to find out new trends and try to bring some new ideas about the brand. It requires scientific skills and creative concepts. Thus, fashion forecasting is a field in the fashion industry that is concerned with predicting upcoming fashion trends in terms of colors, design techniques, textile materials, and more that lead to consumer demand. Fashion forecasters produce trend reports that are used to develop a brand for the production of a product. In the process of making designs, designers need to pay attention to fashion predictions which in addition to having Islamic characteristics, the design can be comparable to international designs.
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Blagu, Diana, Denisa Szabo, Diana Dragomir, Călin Neamțu, and Daniela Popescu. "Offering Carbon Smart Options through Product Development to Meet Customer Expectations." Sustainability 14, no. 16 (August 11, 2022): 9913. http://dx.doi.org/10.3390/su14169913.
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Addressing the global threat of climate change is one of the present priorities of both companies and their customers. Societal trends demand a significant change in consumer behavior in the foreseeable future to contribute to the reduction in carbon emissions reaching the atmosphere, and national and international governments are committing their resources and efforts to this complex endeavor. The current paper addresses the other side of this conundrum, which is how firms can propose carbon-smart alternatives for their products on the market, in order to match the growing interest and the changing behaviors of the consumers. For this purpose, a research and innovation methodology is proposed to expand the design for concept X, namely, the design for sustainability set of guidelines in the area of developing products with a reduced carbon footprint under conditions of timeliness and economic viability. The research is based on refining practical experience and the use of consecrated management techniques and is validated through the employment of a Delphi-based forecasting process. The authors conclude that the large-scale adoption of such recommendations for the various domains of the manufacturing sector has the potential to contribute to climate change mitigation significantly.
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Usman, Syahrul, Jeffry Jeffry, and Firman Aziz. "FORECASTING PENGENDALIAN PERSEDIAAN SUKU CADANG MENGGUNAKAN METODE NAIVE." AL-ULUM: JURNAL SAINS DAN TEKNOLOGI 8, no. 1 (November 28, 2022): 47. http://dx.doi.org/10.31602/ajst.v8i1.8840.
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Inventory control is an important thing that must be considered by every business actor, especially in the retail sector, too much inventory results in increased and inefficient sales time and can even result inlosses. the need to estimate demand and inventory Stock is very necessary to minimize over stock and also under stock to reduce the risk of loss, the ability of retail business actors to predict demand is certainly very helpful in carrying out good inventory management, utilization of transaction data in a certain amount using machine learning methods can be one approach to see consumer behavior trends. The purpose of this study is to analyze and performance testing the forecasting accuracy, using machine learning approach with the Naive method on sales data transaction in automotive companies and then compare the accuracy between the Stock Order Quantity approach methods used so far. The results of this study indicate forecasting accuracy with a forecasting error of 2% (MAPE), This research tries to analyze the time series data of the spare parts sales transaction, predict the future demand, The results of this study indicate forecasting accuracy with error of 2% (MAPE), This is expected to be an added value in inventory management.
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Adnan, Nadia, Shahrina Md Nordin, Imran Rahman, and Amran Md Rasli. "A new era of sustainable transport: An experimental examination on forecasting adoption behavior of EVs among Malaysian consumer." Transportation Research Part A: Policy and Practice 103 (September 2017): 279–95. http://dx.doi.org/10.1016/j.tra.2017.06.010.
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Suryani, Dede Irma, MOH SIDDIK, and MHD IHSAN. "ANALISIS SINGLE EXPONENTIAL SMOOTHING UNTUK MEMPREDIKSI PENJUALAN AYAM." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 9, no. 3 (September 14, 2022): 2363–71. http://dx.doi.org/10.35957/jatisi.v9i3.2889.
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Basically an increase in sales means an increase in sales which is a positive thing for a company, but it will be a problem if the company does not have sufficient product inventory to meet consumer demand. Forecasting is the art and science of predicting future events. Forecasting will involve taking historical data (such as last year's sales) and projecting it into the future using mathematical models. The Single Exponential Smoothing method is a method that has different weighting techniques for the available data. PHP is a programming language that is intended to create web-based applications, and MySQL is the first database supported by a script programming language for the internet php. Therefore, the application of forecasting techniques for the cases experienced by Syahbana Group 2 is the best solution. By utilizing forecasting techniques using the Single Exponential Smoothing method and also using the PHP programming language and mysql database, it is expected to be able to monitor the development of broiler sales in Syahbana Group 2. The results of this study prove that the application of the Single Exponential Smoothing method in forecasting broiler sales makes it easier for Syahbana Group 2 because the forecasting system provides a good accuracy value in providing sales planning for the following month
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Thirumuruganathan, Saravanan, Soon-gyo Jung, Dianne Ramirez Robillos, Joni Salminen, and Bernard J. Jansen. "Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?" Electronic Commerce Research 21, no. 1 (January 13, 2021): 73–100. http://dx.doi.org/10.1007/s10660-021-09457-0.
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AbstractUsing 27 million flight bookings for 2 years from a major international airline company, we built a Next Likely Destination model to ascertain customers’ next flight booking. The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it cannot reasonably make a recommendation. We then compare the performance of the Next Likely Destination model in a real-life consumer study with 35,000 actual airline customers. In the user study, the model obtains a 51% predictive accuracy. What happened? The Individual Behavior Framework theory provides insights into possibly explaining this inconsistency in evaluation outcomes. Research results indicate that algorithmic approaches in competitive industries must account for shifting customer preferences, changes to the travel environment, and confounding business effects rather than relying solely on historical data.
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A S, Dileep Kumar, Ms Sindu D, and Dr Ravikumar G K. "Analyzing and Forecasting the Purchases Made on Black Friday." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 347–56. http://dx.doi.org/10.22214/ijraset.2022.43693.
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Abstract— Things are sold at a substantial discount on the eve of Black Friday, resulting in sales that are 30 times larger than on normal flash sale days. Customers' data from purchases made on this day canbe examined, resulting in a quick declaration of their preferences for specific products. We looked at data that contained packets of clients, and also the factors that influenced their purchases and the amounts they spent. This data is analysed and forecasted purely for the purpose of providing clients with customized discounts on goods depending on individualpreferences and purchase budget. Four models were employed to forecast significant variations in trainingand test data (50:50, 70:30, 30:70), as well as a distinct sample training and testing dataset with two additional examples of prediction: xgboost, tfidftransform, both combination, and extra trees regressor. The two scenarios involve forecasting and analysing another dataset, as well as projected on the train data and testing data on a different testing data set. The dataset would be analyzed to learn about consumer behavior and trends of product the sale's popularity. For each of the five scenarios, the feature significance and benefit importance are displayed.All of the models' accuracy in various settings has been given in the manner of accuracy graphs and the accuracy findings have been displayed in the form of an RMSE score. Keywords— Black Friday, XGBoost, Accuracy.
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Azeem, Abdul, Idris Ismail, Syed Muslim Jameel, Fakhizan Romlie, Kamaluddeen Usman Danyaro, and Saurabh Shukla. "Deterioration of Electrical Load Forecasting Models in a Smart Grid Environment." Sensors 22, no. 12 (June 9, 2022): 4363. http://dx.doi.org/10.3390/s22124363.
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Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15% in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models.
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Patel, Hrishitva, Abdul Samad, Muhammad Hamza, Ayesha Muazzam, and Muhammad Khoiruddin Harahap. "Role of Artificial Intelligence in Livestock and Poultry Farming." Sinkron 7, no. 4 (October 7, 2022): 2425–29. http://dx.doi.org/10.33395/sinkron.v7i4.11837.
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One of the technologies, artificial intelligence (AI), requires quick adoption in the livestock sector. The use of AI technology can be highly beneficial in a number of key areas in the livestock business, including monitoring, forecasting, optimizing the growth of farm animals, contend with pests, diseases, threats of biosecurity, and monitoring farm animals and farm management. Livestock farms will be helped by artificial intelligence to gather and analyses of data in order to precisely forecast consumer behavior, including purchasing patterns, top trends, etc. Operation of farm will be done by using automatic means which directly minimize the expense and increase the quality of egg, milk and meat products but this system needs some extra investment to start.
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V. Akberdina, Victoria, Oksana V. Tretyakova, and Andrey I. Vlasov. "A methodological approach to forecasting spatial distribution of workplaces in an industrial metropolis." Problems and Perspectives in Management 15, no. 4 (December 19, 2017): 50–61. http://dx.doi.org/10.21511/ppm.15(4).2017.05.
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Many world cities retain their unique industrial status. Such a feature of the economy of an industrial metropolis imposes additional requirements on the development of the forecast of spatial distribution of workplaces. The article highlights the contradictions of the long-term development of an industrial megalopolis, which become scenic forks, when forecasted. These include optimization of the industrial and trade-service sectors of the economy, the ratio of inertial and innovative development vectors, variability of migration flows and the choice of the agglomeration model type. The article is devoted to the problem of forecasting the development of a large metropolis, where the industrial sector plays a significant role in the economy. At the methodological level, the article justifies principles of spatial development of an industrial metropolis. The article describes forecasting tools for spatial location of workplaces, based on a combination of several models. The study was performed through the example of Ekaterinburg – the industrial capital of Russia; the metropolis scenarios were justified until 2035; the forecast of spatial distribution was calculated through the example of the two sectors competing for investments – industrial and trade-service. The authors substantiate spatial distribution of workplaces taking into account the projected number of people employed, the number of population of working age and distinguishing features of transport behavior of citizens. The paper demonstrates that the number of large industrial enterprises in a historically industrial center and its first zone decreases, and the modern industry in the form of small and medium-sized businesses located in industrial parks commence gradually forming a circuit with nodes on transport routes towards the largest consumer territories.
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Sulaiman, S. M., P. Aruna Jeyanthy, and D. Devaraj. "Smart Meter Data Analysis Using Big Data Tools." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3629–36. http://dx.doi.org/10.1166/jctn.2019.8338.
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In recent years, the problem of electrical load forecasting gained attention due to the arrival of new measurement technologies that produce electrical energy consumption data at very short intervals of time. Such short term measurements become voluminous in very short time. The availability of big electrical consumption data allows machine learning techniques to be employed to analyze consumption behavior of every consumer on a greater detail. Predicting the consumption of a residential customer is crucial at this point of time because tailor-made consumer-specific tariffs will play a vital role in load balancing process of Utilities. This paper analyzes the electrical consumption of a single residential customer measured using a smart meter that is capable of measuring electrical consumption at circuit level. The issues and challenges in collecting the data and pre-processing required for making them suitable for data analytics are discussed in detail. A comparison of the performance of different machine learning algorithms implemented using Python’s Scikit-learn module gives an insight on the consumption pattern.
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Zhovkovska, Tetyana, Oleksii Bezchasnyi, Olena Usykova, Kostyantyn Rybachuk, and Khrystyna Dzhuryk. "Predicting development based on a model of reflexive connections." Revista Amazonia Investiga 10, no. 42 (July 30, 2021): 113–23. http://dx.doi.org/10.34069/ai/2021.42.06.11.
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The purpose of the article is to develop an approach to quality forecasting of industrial enterprises. This article intends to understand how to take into account in predicting relationship, behavior and interaction of economic agents that affect the efficiency of the enterprise. The result of the work is a reflexive approach to forecasting the development of an industrial enterprise, which focuses on prediction considering the complex interaction of economic agents in industrial activities as subjects of reflection with appropriate ranks. The approach based on the proposed model, which taking into account the reflective relationships between the industrial enterprise system and the components of the external environment, in which the industrial enterprise and other economic agents (or groups of economic agents) are considered as systems and trajectories. Depending on the trajectories of the components of the environment can be predicted development of industrial enterprises and management measures developed for correction. As components of the external environment, the trajectories of which must be taken into account when reflexively forecasting the development of an industrial enterprise are offered: the market of raw materials; groups of competitors; consumer groups; supplier groups; financial market; labor market. The model of taking into account the reflective connections between the system of the industrial enterprise and the components of the external environment is implemented in the PowerSim simulation package.
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Whitaker, Bethany, George Terzis, Eddie Soong, and Wayne Yeh. "Stated Preference as a Tool to Evaluate Airline Passenger Preferences and Priorities." Transportation Research Record: Journal of the Transportation Research Board 1915, no. 1 (January 2005): 55–61. http://dx.doi.org/10.1177/0361198105191500107.
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Stated preference (SP) analysis is a technique widely used by market research and transportation professionals to understand decision-making behavior and consumer choice models. This discussion covers the role of SP as a tool to enhance understanding of air travelers’ preferences and priorities for airline services and the potential for SP research to play a greater role in product development and demand forecasting for different types of airline services. Two case studies of previously conducted research for a major airline in Asia are used to explore how SP data have been used to evaluate passenger preferences and priorities. SP experience gained from other transportation modes (e.g., urban rail and bus services) is also examined and the potential applications of these lessons to the airline industry are suggested.
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Akbar, Taufik, Edi Murdiyanto, and Anita Sumelvia Dewi. "Sentimen Bisnis dan Konsumen dalam Siklus Ekonomi Indonesia." Jurnal Manajemen dan Inovasi (MANOVA) 5, no. 1 (January 28, 2022): 32–47. http://dx.doi.org/10.15642/manova.v5i1.727.
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This research aims to find out the relationship between consumer confidence, business confidence, consumer price index, and composite stock price index in Indonesia because the indicator describes a country's economic cycle as a result of sentiment that occurs. Quantitative descriptive research using multivariate methods of cointegration and granger causality within the VECM statistical framework. The period of the study is from 2015 – 2020. VECM estimation shows that in the long term there is a positive relationship pattern between CCI and itself. Then PMI shows a pattern of positive relationships, while CPI and JCI show negative relationships. Only PMI variables have a significant effect on the CCI in the long run. Then in the short term, PMI has a positive relationship pattern and has a significant influence on CCI and JCI in lags 1 and 2, and has a significant influence on CPI in lag 2. JCI has a positive and significant relationship pattern to CCI and CPI in lag 2. This research still does not discuss the dynamic behavior of the VECM model through the response of each variable to the shock of other variables and forecasting how the response of a variable in the future if a shock occurs in other variables. Governments and central banks must be able to reduce market panic and build a level of public trust. It can be said that optimism from the point of view of the business world and consumers needs to be maintained by stakeholders, especially governments and central banks.
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Lowsky, David J., Donald K. K. Lee, and Stefanos A. Zenios. "Health Savings Accounts: Consumer Contribution Strategies and Policy Implications." MDM Policy & Practice 3, no. 2 (July 2018): 238146831880937. http://dx.doi.org/10.1177/2381468318809373.
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Background. Health savings accounts (HSAs) are tax-advantaged savings accounts available only to households with high-deductible health insurance. This article provides initial answers to two questions: 1) How should a household budget for its annual HSA contributions? 2) Do current contribution limits provide households with the flexibility to use HSAs efficiently? To answer these questions, we formulate the household’s problem as one of determining a contribution strategy for minimizing total expected discounted medical costs. Methods. We use the 2002–2014 Medical Expenditure Panel Survey to develop a novel data-driven model for forecasting a household’s health care costs based on its current cost percentile and other characteristics. A dynamic policy, in which the contribution each year brings the HSA balance up to a household-specific threshold, is derived. This is compared to a simpler static policy in which the target HSA balance is simply the plan’s out-of-pocket maximum, with contributions in any year capped by a limit. Results. We find that: 1) the dynamic policy can save a household up to 19% in costs compared to the static one that is a proxy for typical contribution behavior; and 2) the recommended contribution amounts for 9% to 11% of households in a given year materially exceed what is currently allowed by the federal government. Conclusions. The dynamic policy derived from our data-analytic framework is able to unlock significant tax savings for health care consumers. To allow all households to use HSAs in a tax-efficient manner, a two-tiered contribution policy is needed: Allow unlimited contributions up to some balance, and then impose restrictions thereafter. The resulting impact on overall tax receipts is estimated to be well below what is currently allowed by legislation.
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Wu, Doris Chenguang, Haiyan Song, and Shujie Shen. "New developments in tourism and hotel demand modeling and forecasting." International Journal of Contemporary Hospitality Management 29, no. 1 (January 9, 2017): 507–29. http://dx.doi.org/10.1108/ijchm-05-2015-0249.
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Purpose The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field. Design/methodology/approach Articles on tourism and hotel demand modeling and forecasting published mostly in both science citation index and social sciences citation index journals were identified and analyzed. Findings This review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, whereas disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area. Research limitations/implications The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting. Practical implications This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices. Originality/value The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.
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Kundius, Valentina, O. Rushickaya, Tat'yana Kruzhkova, and Aleksey Ruchkin. "The strategy of economic growth on the example of the export of agricultural organic products." Agrarian Bulletin of the 228, no. 13 (January 16, 2023): 42–49. http://dx.doi.org/10.32417/1997-4868-2023-228-13-42-49.
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Abstract. Purpose. The article deals the issues of marketing research in strategy implementation of economic growth on example of agricultural organic products export to countries of South-East Asia and China. Methods. The authors use statistical data, comparative analysis, and forecasting to verify and confirm their conclusions. The author’s development is based on the method of mathematical modeling. Results. Export outlook of domestic production and consumer behavior were examined. The conclusion was made that the possibility of improvement of a feed quality of the country’s population specifically connected with a level of its life. Scientific novelty. This kind of possibility has become more real with the transition to import substitution and the development of export-oriented agriculture. Especially it is correct for organic agricultural production. It is based on innovative alternative land use and conservation of natural (primarily land) resources, demand for agricultural organic products in countries of South-East Asia and China. Practical significance. Authors also represents the results of marketing research the attitude of consumer behavior at the agricultural organic food products in Russia. These data may be useful for marketing research customers in countries of South-East Asia and China. The results obtained will also be of interest to domestic producers focused on the production of organically pure products, since in combination with the analysis of the consumption market, changes in the values of the population, it is possible to calculate and plan the capacity of the market in the future.
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Kundius, Valentina, O. Rushickaya, Tat'yana Kruzhkova, and Aleksey Ruchkin. "The strategy of economic growth on the example of the export of agricultural organic products." Agrarian Bulletin of the 228, no. 13 (January 17, 2023): 42–49. http://dx.doi.org/10.32417/1997-4868-2022-228-13-42-49.
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Abstract. Purpose. The article deals the issues of marketing research in strategy implementation of economic growth on example of agricultural organic products export to countries of South-East Asia and China. Methods. The authors use statistical data, comparative analysis, and forecasting to verify and confirm their conclusions. The author’s development is based on the method of mathematical modeling. Results. Export outlook of domestic production and consumer behavior were examined. The conclusion was made that the possibility of improvement of a feed quality of the country’s population specifically connected with a level of its life. Scientific novelty. This kind of possibility has become more real with the transition to import substitution and the development of export-oriented agriculture. Especially it is correct for organic agricultural production. It is based on innovative alternative land use and conservation of natural (primarily land) resources, demand for agricultural organic products in countries of South-East Asia and China. Practical significance. Authors also represents the results of marketing research the attitude of consumer behavior at the agricultural organic food products in Russia. These data may be useful for marketing research customers in countries of South-East Asia and China. The results obtained will also be of interest to domestic producers focused on the production of organically pure products, since in combination with the analysis of the consumption market, changes in the values of the population, it is possible to calculate and plan the capacity of the market in the future.
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Ayub, Nasir, Muhammad Irfan, Muhammad Awais, Usman Ali, Tariq Ali, Mohammed Hamdi, Abdullah Alghamdi, and Fazal Muhammad. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler." Energies 13, no. 19 (October 5, 2020): 5193. http://dx.doi.org/10.3390/en13195193.
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Electrical load forecasting provides knowledge about future consumption and generation of electricity. There is a high level of fluctuation behavior between energy generation and consumption. Sometimes, the energy demand of the consumer becomes higher than the energy already generated, and vice versa. Electricity load forecasting provides a monitoring framework for future energy generation, consumption, and making a balance between them. In this paper, we propose a framework, in which deep learning and supervised machine learning techniques are implemented for electricity-load forecasting. A three-step model is proposed, which includes: feature selection, extraction, and classification. The hybrid of Random Forest (RF) and Extreme Gradient Boosting (XGB) is used to calculate features’ importance. The average feature importance of hybrid techniques selects the most relevant and high importance features in the feature selection method. The Recursive Feature Elimination (RFE) method is used to eliminate the irrelevant features in the feature extraction method. The load forecasting is performed with Support Vector Machines (SVM) and a hybrid of Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). The meta-heuristic algorithms, i.e., Grey Wolf Optimization (GWO) and Earth Worm Optimization (EWO) are applied to tune the hyper-parameters of SVM and CNN-GRU, respectively. The accuracy of our enhanced techniques CNN-GRU-EWO and SVM-GWO is 96.33% and 90.67%, respectively. Our proposed techniques CNN-GRU-EWO and SVM-GWO perform 7% and 3% better than the State-Of-The-Art (SOTA). In the end, a comparison with SOTA techniques is performed to show the improvement of the proposed techniques. This comparison showed that the proposed technique performs well and results in the lowest performance error rates and highest accuracy rates as compared to other techniques.
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Platonova, S. I. "BIG DATA AND SOCIAL CONTROL IN EVERYDAY LIFE." Bulletin of Udmurt University. Series Philosophy. Psychology. Pedagogy 32, no. 3 (October 4, 2022): 228–34. http://dx.doi.org/10.35634/2412-9550-2022-32-3-228-234.
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Technological discoveries and the spread of digital technologies require social scientists to revise basic philosophical ideas about the world, society, and man. Issues related to the preservation of freedom, protection of private life and privacy of a person are becoming especially relevant in the digital society. The article considers big data as a tool of social control, supervision and as a means of predicting the behavior of individuals. With the help of big data, large platform companies can shape the core values, cultural codes and consumer behavior of users. The evolution of models of social control is considered: panopticon, synopticon, superpanopticon. While the panopticon is characterized by disciplinary power, the digital society that uses unstructured big data is characterized by instrumental power. The theory of "supervisory capitalism" by S. Zuboff is analyzed and two features of "supervisory capitalism" are distinguished: profit extraction and forecasting of user behavior. In conclusion, having considered the main models of social surveillance, the author highlights the features of social control in a digital society. Social control becomes more subtle, latent, total and indifferent towards a person. The superpanopticon is developing as a social model associated with universal observation based on big data.
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Chatterjee, Swagato, G. Shainesh, and C. N. Sai Sravanan. "Does intention translate into action? Investigating the impact of loyalty intention on future usage." Journal of Indian Business Research 10, no. 2 (June 18, 2018): 151–69. http://dx.doi.org/10.1108/jibr-11-2017-0213.
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Purpose The purpose of the study is to develop a structural and a predictive model of the future purchase behavior of the consumers from value, quality and satisfaction and also finding the role of consumer loyalty in the above-mentioned model. Design/methodology/approach Based on survey and purchase data of a sample of 235 respondents, the authors have used structural equation modeling to develop a structural model and three-stage least square regression to develop and validate the predictive model. Findings In the structural model, the authors found that perceived service quality and network quality leads to customer satisfaction which also leads to loyalty intentions. However, neither past purchase behavior nor loyalty has significant predictive power to predict future usage. But the interaction effect of loyalty and past purchase predicts future purchase significantly. Research limitations/implications The study went beyond structural model and developed a behavioral predictive model which can overcome self-reporting bias. Also, the study focused on the moderating role of loyalty in predicting future purchase quantity, thus contributing toward the theoretical understanding of the effects of loyalty. Practical implications Other than providing a forecasting model, the study helps the service managers to understand the importance of the relational constructs than the tangible constructs. Moreover, it also suggests optimally target the big buyers through the loyalty programs to ensure higher future revenues. Originality/value The study provides new insight on the impact of loyalty intention of consumer’s purchase behavior and shows the boundary conditions of predictive power of loyalty intention and past purchase on future purchase. Moreover, this is one of the very few studies that have focused on these relationships in Indian context.
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Septech, Jassada, Motree Socatiyanurak, and Vorapol Socatiyanurak. "Cashless Economy: The Behavior of Using E-payment in Thailand." Parichart Journal, Thaksin University 36, no. 1 (January 5, 2023): 231–48. http://dx.doi.org/10.55164/pactj.v36i1.259344.
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This research article aims to study the behavior of using e-payment in Thailand, examining the financial factors of: 1) stepping into Thailand's cashless society, 2) people's perceptions of a cashless society, and 3) financial forecasting. In the research, Part 1 results indicate that Thailand is experiencing a growing behavior of using e-payment due to the continuous growth of payment channels through various payment systems and channels. According to the period from 2010 to 2021, a significant leap forward in this regard has been witnessed. Thus, Thailand is likely to transition from a cash-based society to a cashless society. This situation agrees with research by Thomas et al. [1]. In the current research, Part 2 results describe a study of 2,800 respondents from 2015 to 2019 and the effects of this behavior of using e-payments. Exchange and payment all come from consumer decision-making behavior. Since financial technology, or Fintech, is now part of the direction of the development of a cashless society in Thailand, the adoption and use of technology is, therefore, an important factor. The important internal factors include Performance Expectancy, Effort Expectancy, Social Influence, etc., which are consistent with and related to the behavior of using e-payment. From this research, it was concluded that the factors related to the transformation towards the behavior of e-payment in Thailand are the following: 1) personal factors, i.e. gender, age; 2) factors affecting decision-making; 3) factors of acceptance and use; and 4) technology adoption, all of which are factors that correlate with e-payment behavior.
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Mellizo, Philip Pablo. "Do consumers value employee ownership? Evidence from an experimental auction." Journal of Participation and Employee Ownership 1, no. 2/3 (September 10, 2018): 162–90. http://dx.doi.org/10.1108/jpeo-10-2017-0001.
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Purpose The purpose of this paper is to evaluate how the public at large perceives employee ownership, and how public perceptions of employee ownership translate into consumer valuation of goods and/or services produced by employee-owned firms. To the extent that consumer interest regarding the governance and ownership structure of firms matters in their purchasing decision, an employee-owned certification label could be an instrument by firms to segment consumer demand, differentiate products and potentially realize a competitive advantage. Design/methodology/approach Three specific questions are evaluated using the fifth price, experimental Vickrey valuation auction. First, the author obtains estimates of willingness to pay (WTP) premia for a specific item (coffee) differentiated in a controlled setting by the certifications labels that signal various non-market attributes. Specifically, the author examines the WTP premium for coffee that is eligible for the Certified Employee-OwnedSM label, the Fair Trade CertifiedTM Certified label, as well coffee that qualifies for both labels. Second, the author introduces a treatment to evaluate how the provision of information produced by the third party certifiers affects WTP estimates. And third, the author exploits the use of a controlled setting to evaluate how passive sensory information (i.e. taste) may influence the WTP valuation of the labels. Findings WTP premia for coffee carrying only the EO label only increase by 67 cents relative to conventional coffee, which was not significantly different from zero. Bids for both FT and EO&FT labeled coffee were, however, positive ($1.22 and $2.17, respectively) and are also statistically significant. The circulation of information to subjects about the certification programs resulted in increased bids. These bid differences were statistically significant for FT and EOFT coffee, but again, not for EO labeled coffee. Finally, differences in tastes did not appear to drive significant differences in bidding behavior, suggesting that WTP consumer decisions are strongly influenced by non-market attributes. Originality/value Marketers, economists and others have an interest in determining the monetary value individuals place on non-market goods for a variety of reasons; from forecasting new product success to understanding consumer and individual behavior. Unfortunately, many currently available stated preference techniques suffer from hypothetical bias while revealed preference techniques rely on indirect measures. Experimental auctions mitigate some of these issues since they involve individuals exchanging real money for real goods in an active market. WTP valuation has been conducted on a wide variety or products, but none that capture consumer valuation of employee ownership.
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Krishnan R, CV Krishnaveni, and AV Krishna Prasad. "Telecom Churn Prediction using Machine Learning." World Journal of Advanced Engineering Technology and Sciences 7, no. 2 (December 30, 2022): 087–96. http://dx.doi.org/10.30574/wjaets.2022.7.2.0130.
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In every industry, customers are crucial. Customer churn can have a variety of effects and have a negative influence on sales. Analysis and forecasting of customer turnover must be a key component of any business. We will analyze and forecast customer turnover in the telecom industry in our study. The study of consumer behavior is crucial for the telecommunications sector in order to identify those customers who are most likely to cancel their subscriptions. Because there is so much data available and the market is becoming more competitive, businesses are spending more time trying to keep their present consumers than they are trying to win over new ones. The mobile telecommunications market recently transitioned from being one that was expanding quickly to one that was saturated. The goal of telecom companies is to refocus their attention away from attracting new, huge customers and toward retaining existing ones. Knowing which clients are likely to switch to a competitor in the future is important for this reason. Using machine learning techniques such as Decision Tree, Logistic Regression, Random Forest, Gradient Boosted Machine Tree, and Extreme Gradient Boosting, the model is proposed for churn analysis and prediction for telecommunication firms. The performance of various models is also compared. On the basis of the supplied dataset, comparisons are made on the algorithm’s effectiveness.