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Journal articles on the topic 'Consumer learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Chandler, Tomasita M., and Barbara M. Heinzerling. "Learning the Consumer Role: Children as Consumers." Reference Services Review 26, no. 1 (1998): 61–95. http://dx.doi.org/10.1108/00907329810307452.

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2

Steils, Nadia, Alain Decrop, and Dominique Crié. "An exploration into consumers’ e-learning strategies." Journal of Consumer Marketing 36, no. 2 (2019): 276–87. http://dx.doi.org/10.1108/jcm-05-2017-2215.

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Purpose As traditional paper manuals and step-by-step instructions have shown to discourage new product learning because of a lack of exploration, the purpose of this paper is to investigate consumer learning from an online and andragogical, that is, adult learning, perspective by identifying relevant consumer e-learning processes in new product learning. Design/methodology/approach This paper uses thematic and trace analyses on a multi-method data collection, that is, extant e-learning courses, in-depth interviews and non-participant observations. Findings Emerging findings give light on customized, interactive and iterative e-learning processes depending on consumers’ previous experiences, their learning orientation as adult learners and the characteristics of the online environment. Results provide evidence for the existence of three learning strategies and show how the online environment comes shifting traditional consumer learning paradigms. Originality/value This paper contributes to the literature on consumer behavior on two levels. First, the findings highlight the importance of taking an andragogical standpoint to provide a more nuanced and realistic view on consumers’ learning processes in new product learning. Second, the results show how the exploration and interactivity provided by the online environment present beneficial prerequisites for effective consumer learning. More than just being an alternative, online learning is complementary to offline modes of learning to improve consumers’ overall learning experience.

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Aldayel, Mashael, Mourad Ykhlef, and Abeer Al-Nafjan. "Deep Learning for EEG-Based Preference Classification in Neuromarketing." Applied Sciences 10, no. 4 (2020): 1525. http://dx.doi.org/10.3390/app10041525.

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The traditional marketing methodologies (e.g., television commercials and newspaper advertisements) may be unsuccessful at selling products because they do not robustly stimulate the consumers to purchase a particular product. Such conventional marketing methods attempt to determine the attitude of the consumers toward a product, which may not represent the real behavior at the point of purchase. It is likely that the marketers misunderstand the consumer behavior because the predicted attitude does not always reflect the real purchasing behaviors of the consumers. This research study was aimed at bridging the gap between traditional market research, which relies on explicit consumer responses, and neuromarketing research, which reflects the implicit consumer responses. The EEG-based preference recognition in neuromarketing was extensively reviewed. Another gap in neuromarketing research is the lack of extensive data-mining approaches for the prediction and classification of the consumer preferences. Therefore, in this work, a deep-learning approach is adopted to detect the consumer preferences by using EEG signals from the DEAP dataset by considering the power spectral density and valence features. The results demonstrated that, although the proposed deep-learning exhibits a higher accuracy, recall, and precision compared with the k-nearest neighbor and support vector machine algorithms, random forest reaches similar results to deep learning on the same dataset.

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Santoso, Singgih. "Factors influencing the formation of consumer engagement and consumer satisfaction with e-learning activities." Innovative Marketing 17, no. 2 (2021): 137–48. http://dx.doi.org/10.21511/im.17(2).2021.13.

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The COVID-19 pandemic that plagued the world has resulted in many e-learning software that drove virtual learning activities to jump sharply and began to replace face-to-face meetings. This paper aims to find out the influence of digital readiness, technical and information quality, instructor quality, e-learning adoption and attitude on consumer engagement, and consumer satisfaction with e-learning performance. The study was conducted in the form of a quantitative survey at Duta Wacana Christian University in the Special Region of Yogyakarta, Indonesia, over the period from June 2020 to September 2020. The study sample using the purposive random sampling technique consisted of 175 students as respondents. Various statistical methods, including descriptive and structural equation modeling, were used to analyze the data and test the hypotheses of the model. Key findings were that there is a statistically direct impact of digital readiness, technical and information quality, e-learning adoption and attitude, and instructor quality on consumer engagement, and thus consumer engagement influences consumer satisfaction positively and significantly. With these results, tutorial activities need to be implemented for the use of popular e-learning software and related technological literacy, because the need for e-learning software will be even more massive in the future.

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Che, Hai, Tülin Erdem, and T. Sabri Öncü. "Consumer learning and evolution of consumer brand preferences." Quantitative Marketing and Economics 13, no. 3 (2015): 173–202. http://dx.doi.org/10.1007/s11129-015-9158-x.

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Danilāne, Līga. "Students` Learning Outcomes in Consumer Education in Elementary School." SOCIETY, INTEGRATION, EDUCATION. Proceedings of the International Scientific Conference 1 (July 24, 2015): 400. http://dx.doi.org/10.17770/sie2014vol1.777.

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The global economic crisis and the current conditions of market economy require an intelligent, spiritually rich, open-minded, creative, educated, skilled individual who is able to offer himself/herself in the labour market and promote his/her consumer education and participation in society during the economic downturn. Each person faces the increasing diversity of decision making daily in both personal and public life as responsible citizens in a democratic society, effective participants of global economy, knowledgeable consumers, enterprising and productive workers and competent decision makers. The research is carried out within the framework of the PhD thesis "Essence of Consumer Education in Elementary School" that aims to analyze consumer education content within elementary education, pupils' needs in the field of consumer education and create the appropriate learning content. The paper analyzes pupils' learning outcomes in consumer education in the context of sustainability.

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Lee, Jungwon, Okkyung Jung, Yunhye Lee, Ohsung Kim, and Cheol Park. "A Comparison and Interpretation of Machine Learning Algorithm for the Prediction of Online Purchase Conversion." Journal of Theoretical and Applied Electronic Commerce Research 16, no. 5 (2021): 1472–91. http://dx.doi.org/10.3390/jtaer16050083.

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Machine learning technology is recently being applied to various fields. However, in the field of online consumer conversion, research is limited despite the high possibility of machine learning application due to the availability of big data. In this context, we investigate the following three research questions. First, what is the suitable machine learning model for predicting online consumer behavior? Second, what is the good data sampling method for predicting online con-sumer behavior? Third, can we interpret machine learning’s online consumer behavior prediction results? We analyze 374,749 online consumer behavior data from Google Merchandise Store, an online shopping mall, and explore research questions. As a result of the empirical analysis, the performance of the ensemble model eXtreme Gradient Boosting model is most suitable for pre-dicting purchase conversion of online consumers, and oversampling is the best method to mitigate data imbalance bias. In addition, by applying explainable artificial intelligence methods to the context of retargeting advertisements, we investigate which consumers are effective in retargeting advertisements. This study theoretically contributes to the marketing and machine learning lit-erature by exploring and answering the problems that arise when applying machine learning models to predicting online consumer conversion. It also contributes to the online advertising literature by exploring consumer characteristics that are effective for retargeting advertisements.

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Ahluwalia, Amardeep Kaur, and Preeti Sanan. "Consumer Learning in Indian Schools." Asian Journal of Management 8, no. 4 (2017): 1311. http://dx.doi.org/10.5958/2321-5763.2017.00198.6.

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9

Caminal, Ramon, and Xavier Vives. "Price Dynamics and Consumer Learning." Journal of Economics & Management Strategy 8, no. 1 (1999): 95–131. http://dx.doi.org/10.1162/105864099567596.

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10

van Osselaer, Stijn M. J., and Joseph W. Alba. "Consumer Learning and Brand Equity." Journal of Consumer Research 27, no. 1 (2000): 1–16. http://dx.doi.org/10.1086/314305.

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11

Field, John. "Open learning and consumer culture." Open Learning: The Journal of Open, Distance and e-Learning 9, no. 2 (1994): 3–11. http://dx.doi.org/10.1080/0268051940090202.

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Caminal, Ramon, and Xavier Vives. "Price Dynamics and Consumer Learning." Journal of Economics Management Strategy 8, no. 1 (1999): 95–131. http://dx.doi.org/10.1111/j.1430-9134.1999.00095.x.

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13

Garcia, Daniel, and Sandro Shelegia. "Consumer search with observational learning." RAND Journal of Economics 49, no. 1 (2018): 224–53. http://dx.doi.org/10.1111/1756-2171.12224.

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Lei, Yong, Qian Liu, and Stephen Shum. "Warranty pricing with consumer learning." European Journal of Operational Research 263, no. 2 (2017): 596–610. http://dx.doi.org/10.1016/j.ejor.2017.06.024.

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15

Jiang, Zigui, Rongheng Lin, and Fangchun Yang. "A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data." Energies 11, no. 9 (2018): 2235. http://dx.doi.org/10.3390/en11092235.

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Time-series smart meter data can record precisely electricity consumption behaviors of every consumer in the smart grid system. A better understanding of consumption behaviors and an effective consumer categorization based on the similarity of these behaviors can be helpful for flexible demand management and effective energy control. In this paper, we propose a hybrid machine learning model including both unsupervised clustering and supervised classification for categorizing consumers based on the similarity of their typical electricity consumption behaviors. Unsupervised clustering algorithm is used to extract the typical electricity consumption behaviors and perform fuzzy consumer categorization, followed by a proposed novel algorithm to identify distinct consumer categories and their consumption characteristics. Supervised classification algorithm is used to classify new consumers and evaluate the validity of the identified categories. The proposed model is applied to a real dataset of U.S. non-residential consumers collected by smart meters over one year. The results indicate that large or special institutions usually have their distinct consumption characteristics while others such as some medium and small institutions or similar building types may have the same characteristics. Moreover, the comparison results with other methods show the improved performance of the proposed model in terms of category identification and classifying accuracy.

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Hsu, Melissa Yi-Ting, and Julian Ming-Sung Cheng. "fMRI neuromarketing and consumer learning theory." European Journal of Marketing 52, no. 1/2 (2018): 199–223. http://dx.doi.org/10.1108/ejm-12-2016-0866.

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Purpose The purpose of this paper is to examine the impact of gender on the neural substrates of theories on consumer behavior (i.e. the original compared with the revised versions of consumer learning [CL] theory) and to examine whether gender influences brain activation associated with word-of-mouth (WOM) communications (i.e. information specificity, source expertise and tie strength) after a product harm crisis. This article also discusses the WOM effects of product quality perception, negative emotion and purchase intentions by precise localizing brain activity. Design/methodology/approach This study applied functional magnetic resonance imaging to measure brain activity (i.e. the blood oxygen level-dependent signal) during WOM communication after a product harm crisis. Findings The male participants treat the product quality as a constant and tend to support the original CL theory. The female participants, however, showed differentiable brain activation across three factors, suggesting a dynamic representation for product quality (i.e. not a constant), and they appear to be more sensitive to the revised CL theory. Originality/value This paper concluded that the original CL theory applies to males and the revised version applies to females. Therefore, gender determines whether the original or the revised version of the CL theory works in consumers’ decision-making, and the extant of research has not focused on the information after a product harm crisis in terms of whether the information being communicated is specific or tensile through WOM communication.

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17

Cai, Guoliang. "Impact of media use on consumer product knowledge." Social Behavior and Personality: an international journal 48, no. 2 (2020): 11–24. http://dx.doi.org/10.2224/sbp.8558.

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I explored the relationship between media use and consumers' product knowledge. Using survey data obtained from 1,954 consumers of mother and baby products, I found that (a) use of traditional media, generalized network media, and professional network media had a positive impact on consumer product knowledge, and (b) these relationships were moderated by the mothers' stage of learning about maternal and baby products, and perceived risk of the product. Specifically, when a consumer was at a later stage of learning, use of professional network media had a greater impact on product knowledge than did use of the other 2 types of media. Furthermore, when the perceived risk of a product was high, use of traditional media and general network media had a greater impact on product knowledge than did use of professional network media. The findings have practical implications for marketing staff of companies in their selection of media types to post information, and their consideration of consumers' learning stage and perceived risk of products when implementing marketing plans.

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18

Cunha, Marcus, Chris Janiszewski, and Juliano Laran. "Protection of Prior Learning in Complex Consumer Learning Environments." Journal of Consumer Research 34, no. 6 (2008): 850–64. http://dx.doi.org/10.1086/523293.

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19

Christanto Edy, Irwan, and Riyanto -. "Recursive Model: Cognitive Learning Behavior in Online Consumers." International Journal of Engineering & Technology 8, no. 4 (2019): 444. http://dx.doi.org/10.14419/ijet.v8i4.29773.

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Online purchasing decisions are online consumer behavior and are an interesting phenomenon in research. This study aims to prove the concept that online consumer purchasing decisions are influenced by cognitive learning behavior. The main theory underlying this research is consumer behavior and learning. Learning theory is used to analyze consumer learning behavior online with a mix of (crossing) learning theories of behavior and cognitive learning theory. Combination (crossing) between behavioral learning theory, cognitive is called cognitive learning behavior (cognitive learning behavior). This research is a survey. The data used are primary data, with the research instrument in the form of a questionnaire. The subjects of this study are individuals namely online consumers. Online consumers in this study are millennial generation who have made online purchases on one of the e-commerce sites in Indonesia (Matahari.mall, bukalapak, tokopedia, shopee, Zilingo) with this type of product is fashion. In this study 200 respondents were selected. The study consisted of organic stimulation of marketing on the website, online purchasing decisions, cognitive learning, experience preferences. Convenience sampling sampling technique is a sampling method where sampling is based on the availability of elements and the ease of obtaining them. Collecting data with online questionnaires and distributing questionnaires through whatsapp to respondents who are easily contacted by researchers. Data analysis methods with 1) test data quality instruments (validity and reliability), 2) Analysis of Descriptive Statistics and 3) Model Analysis with SEM. The results showed that 1) Organic stimulation of marketing on the website had a positive and significant effect on cognitive learning, 2)experience preference had a positive and significant effect on cognitive learning, 3)cognitive learning had a positive and not significant effect on online purchasing decisions, 4) experience preference had a positive and not significant effect on online purchasing decisions, 5) Organic stimulation of marketing on the website had a positive and significant effect on online purchasing decisions.

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20

Villas-Boas, J. Miguel. "Consumer Learning, Brand Loyalty, and Competition." Marketing Science 23, no. 1 (2004): 134–45. http://dx.doi.org/10.1287/mksc.1030.0044.

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21

Ifrach, Bar, Costis Maglaras, and Marco Scarsini. "Bayesian social learning with consumer reviews." ACM SIGMETRICS Performance Evaluation Review 41, no. 4 (2014): 28. http://dx.doi.org/10.1145/2627534.2627542.

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22

Scarpa, C. "DYNAMIC MONOPOLIST'S BEHAVIOUR AND CONSUMER LEARNING*." Metroeconomica 41, no. 1 (1990): 51–72. http://dx.doi.org/10.1111/j.1467-999x.1990.tb00457.x.

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23

Delbaere, Marjorie, and Malcolm C. Smith. "Health Care Knowledge and Consumer Learning." Health Marketing Quarterly 23, no. 3 (2006): 9–29. http://dx.doi.org/10.1080/07359680802086059.

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24

Fatas-Villafranca, Francisco, Carlos M. Fernández-Márquez, and Francisco J. Vázquez. "Consumer social learning and industrial dynamics." Economics of Innovation and New Technology 28, no. 2 (2018): 119–41. http://dx.doi.org/10.1080/10438599.2018.1433582.

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25

Szmigin, Isabelle, and Marylyn Carrigan. "Learning to love the older consumer." Journal of Consumer Behaviour 1, no. 1 (2001): 22–34. http://dx.doi.org/10.1002/cb.51.

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Ulu, Canan, Dorothée Honhon, and Aydın Alptekinoğlu. "Learning Consumer Tastes Through Dynamic Assortments." Operations Research 60, no. 4 (2012): 833–49. http://dx.doi.org/10.1287/opre.1120.1067.

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Ifrach, Bar, Costis Maglaras, Marco Scarsini, and Anna Zseleva. "Bayesian Social Learning from Consumer Reviews." Operations Research 67, no. 5 (2019): 1209–21. http://dx.doi.org/10.1287/opre.2019.1861.

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28

Gaur, Vishal, and Young-Hoon Park. "Asymmetric Consumer Learning and Inventory Competition." Management Science 53, no. 2 (2007): 227–40. http://dx.doi.org/10.1287/mnsc.1060.0615.

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29

Hopkins, Ed. "Adaptive learning models of consumer behavior." Journal of Economic Behavior & Organization 64, no. 3-4 (2007): 348–68. http://dx.doi.org/10.1016/j.jebo.2006.02.010.

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30

Schumacher, Heiner. "Incentives through consumer learning about tastes." International Journal of Industrial Organization 37 (November 2014): 170–77. http://dx.doi.org/10.1016/j.ijindorg.2014.09.002.

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Heutel, Garth, and Erich Muehlegger. "Consumer Learning and Hybrid Vehicle Adoption." Environmental and Resource Economics 62, no. 1 (2014): 125–61. http://dx.doi.org/10.1007/s10640-014-9819-3.

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32

Israel, Mark. "Services as Experience Goods: An Empirical Examination of Consumer Learning in Automobile Insurance." American Economic Review 95, no. 5 (2005): 1444–63. http://dx.doi.org/10.1257/000282805775014335.

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Theoretical work on experience goods sets out three empirical questions. How accurate is information at initial purchase? How rapidly do consumers learn from product experiences? And how much impact does learning have on purchase decisions? I answer these questions for the case of automobile insurance, using a panel of 18,595 consumers from one firm. My principal findings are: patterns of consumer departures following claims point to learning; consumers enter the firm overly optimistic about its quality and are generally disappointed by experience; and the impact of learning is mitigated by the slow arrival of claims and the development of lock-in.

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Naik, Pratiksha Ashok. "Intelligent Food Recommendation System Using Machine Learning." Volume 5 - 2020, Issue 8 - August 5, no. 8 (2020): 616–19. http://dx.doi.org/10.38124/ijisrt20aug414.

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The buying behavior of the consumer is affected by the suggestions given to the items. Recommendations can be made in the form of a review or ranking given to a specific product. Calories consumed by people contains carbohydrates, fats, proteins, minerals and vitamins, and any malnutrition causes severe health problems. In this paper, we propose a recommendation system which is trained on the basis of the recommendations received by the customer who has already used the product. Software recommends the product to the customer on the basis of the experience of the consumer using the same product. Each person has his or her own eating patterns, based on the preferences and dislikes of the user, indicating that personalized diet is important to sustain the success and health of the user. The proposed recommendation method uses a deep learning algorithm and a genetic algorithm to provide the best possible advice.

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Bose, Mousumi, and Lei Ye. "Cross-cultural perspective of situated learning and coping: understanding psychological closeness as mediator." Journal of Consumer Marketing 37, no. 1 (2019): 10–20. http://dx.doi.org/10.1108/jcm-07-2018-2785.

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Purpose Extant consumer behavior research has alluded to consumer learning; however, little research exists regarding situated learning and its relation to coping with respect to stressful consumption experiences. The purpose of this research is to study situated or in situ learning in two cultural contexts – that of the USA and China. Design/methodology/approach Online data were collected from non-students in both the USA and China, and structural equations modeling was used to analyze data. Findings Results demonstrated that situated learning helped cope better with stressful episodes for both cultures. Psychological closeness to the problem mediated the relationship between the antecedents and situated learning for US consumers more than for Chinese consumers. Research limitations/implications Since US consumers tend to be psychologically close to the stressor during the consumption process, firms should preemptively inform and educate them about potential stressors to help them learn and cope. However, as Chinese consumers tend not to be psychologically close to the problem, they need to be dealt differently. Originality/value This research provides a holistic view of situated learning and coping as a process involving consumers, firms and situations and examines their underlying factors in stressful consumption encounters. It establishes the mediating role of psychological closeness between antecedents and consumers’ situated learning and explores the differences of psychological closeness in two different cultures, that of the USA and China.

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Aleti, Torgeir, Jasmina Ilicic, and Paul Harrigan. "Consumer socialization agency in tourism decisions." Journal of Vacation Marketing 24, no. 3 (2017): 234–46. http://dx.doi.org/10.1177/1356766717700190.

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This study introduces consumer socialization agency (CSA; i.e. the act of influencing another about consumption) as the reason why consumers learn through peer communication on social media tourism sites. Based on an online panel of 193 US consumers, the study investigated how a personal connection to a tourism site (i.e. customer engagement [CE]) and a connection with peers on social media (i.e. peer group identification) drives CSA about tourism, which, subsequently, influences learning about tourism-related consumption decisions (i.e. peer communication). Our model establishes that identification with peers on social media and CE with tourism sites are antecedents to consumer socialization. Consumers need to feel engaged with tourism social media sites to participate in socialization and feel connected to their peers on social media in general. Consumer socialization, or the willingness to teach/influence tourism-related skills to friends, influences the willingness to learn new tourism consumer skills, including tourism-related decision-making. We propose that for a tourism site to be successful, it must enable social exchange of knowledge and ideas (through enabling consumer socialization), not just individual user experience.

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Opitz, Ina, Kathrin Specht, Annette Piorr, Rosemarie Siebert, and Ingo Zasada. "Effects of consumer-producer interactions in alternative food networks on consumers’ learning about food and agriculture." Moravian Geographical Reports 25, no. 3 (2017): 181–91. http://dx.doi.org/10.1515/mgr-2017-0016.

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Abstract In the recent literature, Alternative Food Networks (AFN) are discussed as a promising approach, at the urban-rural interface, to meeting the challenges of the current agri-food system. Consumer-producer collaboration is seen as a characteristic feature in this context. What is lacking, however, are general concepts for describing the topics of consumer-producer interactions (CPI). The present study aims (1) to develop an analytical framework relying on six CPI domains and (2) to apply it to investigate CPI effects on consumers’ learning about and appreciation of agriculture. We conducted 26 guided interviews with consumers and producers of the three most frequent AFN types in Germany: community-supported agriculture (CSA), food coops, and self-harvest gardens. The results show that AFN participation enhances consumers’ learning about food (seasonality, cooking/nutrition, housekeeping aspects) and agricultural production (farmers’ perspectives, cultivation). Our results show that consumer’s learning is influenced by certain CPI domains, and each AFN type can be described by distinctive CPI domains. This led to the conclusion that specific AFN types open up specific learning channels and contents, with consumers learning from producers. AFNs at the urban-rural interface exploit knowledge of rurality.

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Song, Xiao Qing, Xi Xiang Sun, and Yan Yan He. "Behavior Based on Product Concept Learning." Applied Mechanics and Materials 539 (July 2014): 931–38. http://dx.doi.org/10.4028/www.scientific.net/amm.539.931.

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Form consumer learning perspective, a theoretical analysis of the structure of brand knowledge is gave. Product concepts will be transformed into knowledge nodes, association links and affective response which are stored in consumer memory. Consumer brand knowledge is composed of brand awareness, brand image and brand attitude. Data are collected from questionnaire. The structure of brand knowledge is confirmed by exploratory factor analysis. Form a binary logistic regression analysis of brand knowledge and consumer purchase behavior, it found that brand attitude is the main factor to predict and explain the purchase behavior based on product concepts.

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Ossani, Paulo César, Diogo Francisco Rossoni, Marcelo Ângelo Cirillo, and Flávio Meira Borém. "Classification of specialty coffees using machine learning techniques." Research, Society and Development 10, no. 5 (2021): e13110514732. http://dx.doi.org/10.33448/rsd-v10i5.14732.

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Specialty coffees have a big importance in the economic scenario, and its sensory quality is appreciated by the productive sector and by the market. Researches have been constantly carried out in the search for better blends in order to add value and differentiate prices according to the product quality. To accomplish that, new methodologies must be explored, taking into consideration factors that might differentiate the particularities of each consumer and/or product. Thus, this article suggests the use of the machine learning technique in the construction of supervised classification and identification models. In a sensory evaluation test for consumer acceptance using four classes of specialty coffees, applied to four groups of trained and untrained consumers, features such as flavor, body, sweetness and general grade were evaluated. The use of machine learning is viable because it allows the classification and identification of specialty coffees produced in different altitudes and different processing methods.

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Grubb, Michael D., and Matthew Osborne. "Cellular Service Demand: Biased Beliefs, Learning, and Bill Shock." American Economic Review 105, no. 1 (2015): 234–71. http://dx.doi.org/10.1257/aer.20120283.

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Following FCC pressure to end bill shock, cellular carriers now alert customers when they exceed usage allowances. We estimate a model of plan choice, usage, and learning using a 2002–2004 panel of cellular bills. Accounting for firm price adjustment, we predict that implementing alerts in 2002–2004 would have lowered average annual consumer welfare by $33. We show that consumers are inattentive to past usage, meaning that bill-shock alerts are informative. Additionally, our estimates imply that consumers are overconfident, underestimating the variance of future calling. Overconfidence costs consumers $76 annually at 2002–2004 prices. Absent overconfidence, alerts would have little to no effect. (JEL D12, D18, L11, L96, L98)

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Voronenko, Yuriy V., Olesya P. Hulchiy, Iryna M. Khomenko, Nadiia M. Zakharova, and Kostyantyn V. Balashov. "METHODS FOR COMMUNICATION PROCESSES ENHANCEMENT IN THE “PROVIDER-CONSUMER (LEARNER)” SYSTEM OF EDUCATIONAL SERVICES." Wiadomości Lekarskie 73, no. 8 (2020): 1663–67. http://dx.doi.org/10.36740/wlek202008114.

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The aim: To determine learners’ (doctors) needs and draw up proposals for upgrading educational communication processes in the “provider-consumer” system of educational services. Materials and methods: The biblio-semantic, biostatistic and sociological methods were used. 754 author questionnaires were processed. Results: The socio-professional characteristics of learning service consumers at Shupyk National Medical Academy of Postgraduate Education were analyzed as well as studied their proposals. Evaluating communication interactions in the “provider-consumer” system of educational services concerned determining the most comfortable organizational modes of education for a consumer, making a careful analysis of the learning service characteristics by “accessibility” and “effectiveness” criteria. The determination methodology and research results can be used by universities and colleges regardless of the educational program specialization. Conclusions: Ensuring the educational communication processes effectiveness in the “provider-consumer” system of educational services is achieved by studying learners’ adaptation according to such principal criteria as organizational modes of education, the learning service accessibility and effectiveness. The learning service providers adjust customer requirements to the capabilities of a particular educational institution (personnel availability, facilities and resources etc.) and so that they ensure competitiveness.

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Wijayanti, Rena Feri, Tri Yulistyawati Evelina, and Joni Dwi Pribadi. "ANALISIS PENGARUH IBU SEBAGAI CONSUMER SOCIALIAZATION AGENT PADA KEPUTUSAN PEMBELIAN OLEH KONSUMEN REMAJA." Adbis: Jurnal Administrasi dan Bisnis 11, no. 1 (2017): 15. http://dx.doi.org/10.33795/j-adbis.v11i1.12.

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Consumer behavior is formed through various processes that have been passed since the early age. The initial process that contributes greatly in the formation of consumer behavior is at the stage of socialization conducted by an individual in the family environment. The family becomes the starting place of learning is done in addition to the influence of the surrounding environment or peers. Adolescence is one category of consumer age that has the potential to be the target consumers. Based on the socialization process experienced by adolescents it will be seen how their behavior becomes a consumer who can make decisions in making purchases.

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Choi, Jaeun, and Yongsung Kim. "A Heterogeneous Learning Framework for Over-the-Top Consumer Analysis Reflecting the Actual Market Environment." Applied Sciences 11, no. 11 (2021): 4783. http://dx.doi.org/10.3390/app11114783.

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The over-the-top (OTT) market for media consumption over wired and wireless Internet is growing. It is, therefore, crucial that service providers and carriers participating in the OTT market analyze consumer traffic for pricing, service delivery, infrastructure investments, etc. The OTT market has many consumer groups, but the proportion of users is not consistent in each. Furthermore, as multimedia consumption has increased owing to the COVID-19 epidemic, the OTT market has changed rapidly. If this is not reflected, the analysis will not be accurate. Therefore, we propose a framework that can classify consumers well based on actual OTT market environment conditions. First, by applying our proposed conditional probability-based method to basic machine learning techniques, such as support vector machine, k-nearest neighbor, and decision tree, we can improve the classification performance, even for an imbalanced OTT consumer distribution. Then, it is possible to analyze the changing consumer trends by dynamically retraining the incoming OTT consumer data. Conventional methods result in low classification accuracy in low-number classes, but our method shows an improvement of 5.3–19.2% based on recall. Moreover, conventional methods have shown large fluctuations in performance as the OTT market environment has changed, but our framework consistently maintains high performance.

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Huang, Yan, Param Vir Singh, and Kannan Srinivasan. "Crowdsourcing New Product Ideas Under Consumer Learning." Management Science 60, no. 9 (2014): 2138–59. http://dx.doi.org/10.1287/mnsc.2013.1879.

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Rossi, Carla. "Online consumer communities, collaborative learning and innovation." Measuring Business Excellence 15, no. 3 (2011): 46–62. http://dx.doi.org/10.1108/13683041111161157.

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Field, John. "Open learning, consumer culture and social exclusion." Open Learning: The Journal of Open, Distance and e-Learning 11, no. 1 (1996): 54–58. http://dx.doi.org/10.1080/0268051960110106.

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Sung, Yen-Ching. "Consumer learning behavior in choosing electric motorcycles." Transportation Planning and Technology 33, no. 2 (2010): 139–55. http://dx.doi.org/10.1080/03081061003643747.

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Young, Murray A., and Paul L. Sauer. "Organizational learning and online consumer information services." Journal of Consumer Marketing 13, no. 5 (1996): 35–46. http://dx.doi.org/10.1108/07363769610130864.

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Simon, Florence, and Meera Roy. "Consumer Audit of Community Learning Disability Teams." British Journal of Learning Disabilities 24, no. 4 (1996): 145–49. http://dx.doi.org/10.1111/j.1468-3156.1996.tb00223.x.

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Li, Xiaoqing. "Agent-based consumer learning in e-commerce." International Journal of Networking and Virtual Organisations 4, no. 1 (2007): 65. http://dx.doi.org/10.1504/ijnvo.2007.012083.

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Bergiel, Blaise J., and Christine Trosclair. "INSTRUMENTAL LEARNING: ITS APPLICATION TO CONSUMER SATISFACTION." Journal of Consumer Marketing 2, no. 4 (1985): 23–28. http://dx.doi.org/10.1108/eb008141.

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