Research on online consumer behavior and psychology under the background of big data

Chen, Wenting; Zhang, Qian; Jin, Maozhu; Yang, Jie

doi:10.1002/cpe.4852

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…When this model is used to analyze consumer behavior, it points out that consumer purchasing behavior is caused by stimulus, which come from physiological factors, psychological factors and external environment. Therefore, it is frequently employed to examine how customers behave when making purchases in an online shopping environment, with a primary focus on the motivating factors that influence consumer behavior [13].…”

Section: Scenario 3: Sor Model Based On Big Datamentioning

confidence: 99%

Analysis of implementation for big data techniques in consumer behavior

Liu

2024

ACE

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Consumer behavior can refer to both the purchasing behavior of consumers towards goods and the decision-making behavior of consumers when choosing goods, the research in this field involves psychology, marketing, and other disciplines. As a popular data mining and processing technology, big data technology has powerful information analysis capabilities. Therefore, with this in mind, applying big data technology to consumer behavior research can greatly improve the efficiency. This study focuses on the implementation for big data techniques in consumer behavior, including application scenarios, application methods, as well as application efficiency. The AIDMA, AISAS, and SOR models are suitable for using big data analysis technology. Big data technology helps enterprises influence external factors in consumer behavior factors, thereby influencing consumer decision-making behavior through internal perception factors. Overall, these results aim to collect and summarize relevant knowledge about consumer behavior and big data for future scholars to investigate and explore the implementation schemes.

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Section: Scenario 3: Sor Model Based On Big Datamentioning

confidence: 99%

Analysis of implementation for big data techniques in consumer behavior

Liu

2024

ACE

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“…Hwang and Jeong (2014) reviewed studies on online purchase behaviour research published after IMDS 121,2 2000 in major management information systems (MIS) and electronic commerce journals. Chen et al (2018) analysed consumer behaviour, such as psychological characteristics, behavioural characteristics, shopping needs, shopping motivations, price perceptions and risk perceptions, in reference to big data. Hofacker et al (2016) show that data analysis has the potential to further our understanding of each stage of the consumer decision-making process.…”

Section: Partially Strategic Consumer Behaviourmentioning

confidence: 99%

Optimal pricing and ordering policies with different patience levels of consumers

Tang

Song

et al. 2021

IMDS

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PurposeUsing information systems via data mining and cluster analysis technologies, consumers' strategic behaviour can be measured, and their patience levels can be accurately described. This paper investigates the retailer's pricing and ordering policies when facing strategic consumers with different levels of patience and discusses the impacts of consumers' patience levels and proportions on retailers' maximum expected profits.Design/methodology/approachBy cluster analysing transaction data on the number of websites visited, browsing time and purchase decision time, consumers' patience levels can be obtained. The authors formulate a newsvendor model considering customers' different patience levels. Three scenarios are investigated: two segments of consumers with two different levels of patience (Scenario I), multiple segments of consumers with different levels of patience (Scenario II) and a continuum of consumers whose levels of patience follow a continuous distribution (Scenario III). Then, general formulas are deduced for retailers' optimal prices, ordering quantities and profits.FindingsUnder Scenario I, if the proportion of less patient consumers is greater (less) than a threshold, the retailer's optimal price is equal to the less (more) patient consumers' reserve price. Under Scenario II, once the proportion of fully strategic consumers exceeds a certain threshold, the retailers' optimal price is equal to the fully strategic consumers' reserve price regardless of consumers' patience levels and proportions. Under Scenario III, the retailer's pricing and ordering policies depend on the distribution of their patience level.Originality/valueFew studies have considered consumers' different levels of patience when making retailer pricing and ordering decisions. In this paper, strategic consumer behaviour is measured, and consumers' patience levels and proportions are obtained by cluster analysing consumer transaction data recorded by an information system. Three scenarios in which strategic consumers may be heterogeneous and have different patience levels are investigated. The results can guide retailer decision-making.

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“…• Personal cultural and spiritual enjoyment As the tangible attributes of products can meet consumers' functional needs, the cultural attributes attached to products can satisfy the spiritual needs of consumers, and cultural products have rich cultural connotations, so cultural production should fundamentally meet the growing spiritual and cultural demands of human beings [22] [23] . The improvement of material level makes consumers' individual needs more and more obvious, through the analysis of the psychological characteristics of the consumers, we can understand the willingness of consumers to buy [24] . On this basis, Li Siqu proposed that the cultural connotation of modern goods should not only realize the spiritual satisfaction of consumers, but also recognize their social identity [25] .…”

Section: Theme3: the Function And Impact Of Cultural Added Valuementioning

confidence: 99%

A Review of Cultural Added Value Research Based on Knowledge Mapping Analysis

Yudong

et al. 2020

E3S Web Conf.

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The enhancement of cultural added value to products is reflected in the level of personal cultural spiritual enjoyment, corporate cultural brand building and social cultural essence inheritance. Compared with ordinary products, the added value of products after incorporating cultural elements is the size of the product’s cultural added value. At the same time, different components of cultural added value can meet different cultural needs of consumers and reach emotional consensus with consumers to some extent. This complex diversity of value realization helps to lay a good foundation for developing cultural brands with Chinese characteristics and transmitting excellent traditional Chinese culture.

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Research on online consumer behavior and psychology under the background of big data

Cited by 7 publications

References 3 publications

Analysis of implementation for big data techniques in consumer behavior

Analysis of implementation for big data techniques in consumer behavior

Optimal pricing and ordering policies with different patience levels of consumers

A Review of Cultural Added Value Research Based on Knowledge Mapping Analysis

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