Shopping Behavior Research Articles

Estimating family shopping behavior: A multi‐perspective approach to understanding grocery shopping

AbstractThis study examines weekly shopping trips of young families using a multi‐perspective approach that considers psychological personality traits, situational influences, and socio‐demographic factors to explain food purchases. Over a period of 4 weeks, the data of 60 shoppers and their families as well as 217 purchases were recorded using questionnaires and receipts. Analysis showed that personality traits and socio‐demographic data are good predictors of food purchases. The study provides insights into the complexity of shopping behavior, shows the need for multi‐perspective approaches, and enables the derivation of strategies to optimize healthy shopping behavior for young families.

Research on the Impact of Social Media on Consumer Shopping Behavior:Based on Case Analysis of the Adolescent Population

Starting from the underlying logic of the impact of social media on consumers, this article focuses on analyzing the shopping habits of young consumers and the influence and shaping of social media on their consumption behavior. It analyzes the positive and negative effects of social media and puts forward relevant opinions and suggestions. Compared to the adult consumer group, the adolescent consumer group has not yet formed a relatively fixed consumption value system, and their consumption behavior is more susceptible to various external factors. Adolescents are in a critical period of character development and self-value establishment, so they will pay attention to social trends and seek the unique value of their own existence during this stage. Social media, as a fast channel for transmitting information, is also the main communication platform for adolescents. It provides a huge amount of information, including product purchasing channels, consumer evaluations, etc. These pieces of information constantly influence adolescents’ thoughts and subtly shape their shopping behavior. This article advocates for the correct guidance and shaping of the values of young people, exercising and cultivating their independent thinking ability, to enable them to have healthy and good consumption behavior.

Design of determinants of online shopping intentions and behavior of students at private universities in East Jakarta 2024

Globalization and technological advances, especially the internet, have changed people's shopping patterns. Online shopping is now a trend, allowing easy access to products at any time. The Covid-19 pandemic has accelerated this transition, with 74.5% of consumers preferring to shop online rather than offline, creating a new profitable consumption pattern. This study aims to identify significant relationships between latent variables that influence online shopping behavior, including Online Shopping Motivation, Subjective Norms, Perceived Behavioral Control, Consumer Knowledge, Online Shopping Intention, and Online Shopping Behavior. This research design uses a quantitative approach with SEM-PLS to analyze the relationship between variables that influence online shopping behavior of students at Private Universities in East Jakarta, providing an in-depth understanding for e-commerce in more effective marketing strategies. The results of the analysis show that Perceived Behavioral Control plays an important role in increasing online shopping knowledge and intention, supporting the theory of Planned Behavior. In addition, Subjective Norms are proven to influence consumer knowledge and intention, reflecting the impact of the social environment on shopping decisions. Although there is a positive relationship between Online Shopping Intention and Online Shopping Behavior, the results show that intentions are not always realized in action, indicating the complexity of consumer behavior. The implications of this study include the importance of consumer education to increase perceived control, leveraging social norms in marketing strategies through collaboration with influencers, and attention to other factors that influence shopping decisions. This study highlights the need to develop more effective marketing strategies, based on a deeper understanding of the relationships between variables, to increase consumer engagement and conversion on online shopping platforms. These findings contribute to academic and practical understanding in the context of shopping behavior in the digital era.

Food insecurity among food banks users during the COVID-19 pandemic in Germany – what have we learnt and what should we do?

Abstract Background Before the start of the COVID-19 pandemic in 2019, up to 1.65 million people visited a total of 956 food banks called “Tafel” in Germany. The start of the pandemic and the associated first lockdown led to shortages in commercial food outlets and to considerable restrictions on the range of food banks. The aim of the study “Food INsecurity Among TAfel users” (FINATA) was to investigate the prevalence of food insecurity (FI) and its association with social support and food shopping behaviours among Tafel users during the pandemic. Methods A written and/or oral survey was conducted in 2020 and 2021 among a total of 985 Tafel users. In addition to descriptive analyses, regression analyses, stratified for 2020 and 2021, were conducted to investigate associations between FI and the other variables. Results Participants of 2020 (n = 565) and 2021 (n = 420) differed in several socio-demographic characteristics. Whereas the prevalence of moderate FI over the 30 days prior to the survey differed between participants of 2020 (21,6 %) and 2021 (6,6 %), the prevalence of severe FI was almost identical (3,9 % vs. 4,0 %). Variables being significantly (p < 0,05) associated with moderate/severe FI included being single vs. being married (2020: OR 4,89, 2021: OR 2,69), receiving at least half of food in the household from the Tafel (2020: OR 0,34), low vs. moderate/high social support (2020: OR 2,93), buying food in commercial food outlets at least once/week vs. less often (2021: OR 0,38) and time of travel to the most used commercial food outlet (2021: OR 1,04). Conclusions The social safety net during the pandemic failed to enable “all people to have (...) economic access to sufficient (...) food” in Germany. Future studies should investigate the relationship between social support, shopping behaviours and FI at the long-term. Food banks are recommended to introduce measures to improve the social situation of their clients such as cooperating with social cafés and restaurants.

What drives social shopping in social commerce platform? The effects of affordance, self-congruity and functional-congruity

PurposeSocial group system and social tagging system, which leverage the psychological mechanisms underlying group influence and social tags to drive consumer behaviors, have been prevalent in the social commerce platform. However, limited studies have examined how the affordances of social group system and social tagging system influence consumers’ social shopping behavior. The purpose of this study is to examine the formation of social shopping behavior in the social commerce platform.Design/methodology/approachCombining affordance theory with dual-congruity theory, we develop a model to examine how the affordances of social group system and social tagging system influence consumers’ social shopping behavior through the underlying self-congruity and functional-congruity processes. We empirically validate the research model using a multimethod approach, including an instrument development study and a field survey study.FindingsOur empirical findings show that social support positively influences relational identity, while it has a nonsignificant effect on social identity. Social interactivity positively influences relational identity and social identity. Furthermore, social tagging quality and social endorser credibility positively affect perceived diagnosticity and perceived serendipity. Finally, relational identity, social identity, perceived diagnosticity and perceived serendipity collectively determine consumers’ social shopping intention.Originality/valueThis study contributes to the theoretical understanding of social shopping in social commerce and offers practical implications for designing an effective social group system and social tagging system to boost product sales.

Exploring the Determinants of Chinese Tourists' Shopping Behavior During Shopping Tourism in Korea.

This study aimed to examine the factors influencing Chinese tourists' shopping behavior during shopping tourism in Korea using a theoretical framework based on the modified theory of planned behavior (TPB). A quantitative research method was used; the questionnaire for this study was posted on Wenjuanxing for data collection and 269 valid questionnaires were analyzed in September 2024 using partial least-squares structural equation modeling (PLS-SEM). The results of analyzing the 269 questionnaires showed that attitude, conformity, perceived behavioral control, and perceived quality of goods influenced shopping intention. However, face consciousness did not affect shopping intentions. Additionally, Chinese tourists' shopping intentions influenced their shopping behavior during shopping tourism in Korea. The results of this study reveal the potential mechanisms of the influence of shopping intentions with conformity (a factor of Chinese-subjective norms) and the perceived quality of goods (a new factor) in shopping behavior, and they provide theoretical guidance and appropriate marketing strategies for companies in the Korean tourism field.

Modelling household online shopping and home delivery demand using latent class & ordinal generalized extreme value (GEV) models

The surge in e-commerce during the past decade has led to dramatic changes in consumer shopping behaviour. The study applies two Generalized Extreme Value (GEV) family models to investigate households' e-shopping demands. The study proposes a model structure to jointly model ordinal-based choice behaviour with choice-makers' latent class membership. Introducing latent class structure with the OGEV formulation accounts for the relationship between choice-makers heterogeneous preferential groups and their ordinal choice outcomes. Furthermore, the study also applies the Ordered General Extreme Value (OGEV)-Negative Binomial (NB) model, capturing the interplay between consumers' in-store shopping demands and online shopping behaviour. The RUM principle inherited within the OGEV-NB model allows econometric valuation of in-store shopping activity explicitly considering households' e-shopping demands. Both models are empirically estimated using a dataset collected in the Greater Toronto Area (GTA), Canada. The empirical findings and behavioural implications are also discussed.

How thinking style influences online consumers’ responses to compromise-based service attributes

ABSTRACT Prior studies on online shopping behavior have been silent about global consumers’ compromise choices in online service attributes. Three studies investigate how and when consumers’ thinking styles affect their service responses toward online retail websites offering compromise options. The priming findings demonstrate that consumers in two cultural groups (Korea and France) are more satisfied with and more likely to repurchase from compromise-based websites in the holistic-thinking than analytic-thinking condition. This research contributes to extant literature on consumer online behavior and provides managerial insights on the practical uses of compromise tendencies for online service strategies in global retail markets.

Adaptation and resilience in retail: Exploring consumer clusters in the new normal

PurposeThis research delves into the diverse responses of individuals to the challenges of the COVID-19 pandemic and subsequent shopping behaviour. DesignBased on the K-means clustering algorithm, a comprehensive analysis of survey data identifies distinct consumer clusters based on their adaptive capacities, resilience, and shopping preferences. The study encompasses a broad demographic spectrum, examining various aspects such as age, urban or rural residence, education, household size, and income. FindingsThe findings identify several clusters of consumers showing specific behaviour during the COVID-19 pandemic and their subsequent shopping behaviours, unveiling distinct profiles of adaptation, resilience, and shopping preferences. The K-means clustering algorithm allows the identification of four distinct consumer clusters. ImplicationsThe results have significant implications for understanding consumer behaviour, tailoring marketing strategies, and shaping the future of retail under the New Normal. Each cluster's characteristics provide valuable insights for businesses to align strategies with evolving consumer needs, considering both demographic factors and shopping preferences. OriginalityThe research contributes to the understanding of consumer behaviour during times of crisis, providing relevant insights from a theoretical perspective based on the K-means clustering algorithm.

Gender-Based Analysis of Online Shopping Patterns on Shopee in Malaysia: A J48 Decision Tree Approach

The purpose of this study is to investigates the gender differences of Shopee platform for online shopping behavior by using the J48 decision tree algorithm to classify and predict shopping frequency among male and female consumers for Malaysia context. WEKA software was used in this study to analyze the datasets. From the experiments, the majority of Shopee user were female consumers. The findings shows that female consumer behavior is more complicated and more varied regarding purchasing behavior. The study's findings demonstrate the potential of gender specific insights to enhance e-commerce strategies, particularly in product recommendations and targeted marketing. Although the J48 model performed well in predicting male shopping patterns, it was less effective for females, indicating the need for more advanced modeling techniques is used to better capture the complexities of female consumer behavior. This research also emphasizes the significance of using machine learning tools like the J48 decision tree to analyze consumer data, providing valuable insights for improving customer satisfaction and business performance. However, limitations such as sample size and the focus on a single platform suggest that further research is needed, including the exploration of alternative algorithms and broader demographic factors.

Pengaruh Literasi Halal, Religiusitas, Halal Awareness Terhadap Minat Beli Konsumen pada Restoran Bersertifikat Halal pada Generasi Z di Jabodetabek

This research aims to investigate the influence of halal literacy, religiosity, and awareness of halal on consumer buying interest in halal certified restaurants in the Jabodetabek area, with a focus on Generation Z. Data was collected through a survey targeting 162 respondents using a scale of 1 to 6. Testing was carried out using the Smart PLS V.3 analysis tool and the Structural Equation Modeling (SEM) analysis approach. The results of the analysis show that halal literacy has a positive but not significant influence on consumer buying interest, while religiosity and halal awareness will have a positive and significant influence on consumer buying interest. These findings indicate that religious understanding and awareness of halal play an important role in influencing consumer shopping behavior, especially in the context of halal certification. The implications of these findings can help halal food and beverage industry stakeholders in developing more effective marketing strategies to attract and retain their potential customers amidst increasingly fierce competition in the industry.

REVIEW PAPER ON E-COMMERCE

Consumer’s preference towards online shopping is increasing day by day as evolution of smartphone and internet connectivity. This trend will continue in upcoming time. This review paper on e-commerce identifies about the scenario of e-commerce consumer’s profile. This paper compares online and traditional shopping behaviour of consumer, perceived risk and influencing of e-commerce. Last part of this paper is about advantages and disadvantages of e-commerce from the consumer’s perspective.

Maximizing Marketing Value: An Empirical Study on the Framework for Assessing AI and ML Integration in Marketing Management

To address this issue, we conduct an empirical investigation into the application of AI and ML in marketing management using a rich framework that aims to optimize marketing value as much as possible. The framework is structured in four pillars: data gathering and processing, customer insights & segmentation, personalized marketing strategies and performance improvement. The utility and practical implementation of the framework was examined through a mixed-methods study design employing quantitative surveys validated by qualitative interviews. The survey was conducted among marketing practitioners across all industries. We also analyze the extent of AI and ML integration in each component empirically, with qualitative insights providing perspectives on opportunity areas, challenges and best practices. Three of these areas in the framework organizational readiness, resource allocations and skill gaps are highlighted as those with most pronounced differences on AI & ML deployment. To learn more about the dataset, demographics of 38 respondents in sample. The sample has a diversified age distribution and it ranges from 27 to 50 years of age. This demonstrates a significant bit of variation (SD = 7.30) and an average age of around 36-37 years Because there's also diversity when it comes to gender representation, with seven people identifying as "Other", besides the six male and fifteen women. According to the statistics, most people are bachelor’s degree holders although also a lot of masters and doctorate degrees which adds depth into information. Overall, the results provide a glimpse into demographic attributes regarding marketing performance and providing useful information for strategic decision making by doing so on behalf of management in charge with implementing those decisions. Finally, this research provides actual data rather than theory on how AI and ML mechanics are being integrated in the marketing management space. The strategic deployment of AI and ML in this research can significantly improve the efficient utilization of resources, maximize marketing efficiency by displaying key parameters impacting shopping behaviour as possesses with more long standing competitive benefits. When they can lean on hard data, researchers, marketers and decision-makers alike are likely to find a more straightforward path to the tasks that matter most as well as how AI/ML factors into driving real marketing value. The research also underscores the importance of ongoing learning, adapting and aligning strategies to leverage new technology in an ever-evolving field like marketing.

Assessment of Women's Online Shopping Behavior in India: Model Design and Analysis

The exponential increase in internet usage in India has driven the swift growth of e-commerce, with women playing a crucial role in this expanding digital economy. This research presents a thorough literature analysis and framework to examine the factors affecting the online purchasing behaviour of women in India. The study specifically attempts to examine the influence of psychological, social, cultural, economic, privacy and security, and technological aspects on the purchasing decisions of female consumers. This research underscores the difficulties women encounter in online purchasing, especially in apparel and cosmetics, providing a gender-specific perspective frequently overlooked in existing studies. This study's originality stems from its integrative methodology, synthesising findings from established consumer behaviour theories and applying them to the context of women's online buying practices in India, an area that has been insufficiently explored. By addressing these gaps, the study enhances the theoretical comprehension of women’s digital consumer behaviour and establishes a foundation for future practical strategies that businesses, marketers, and policymakers can utilise to improve the online shopping experience for Indian women.

Exploring the Impact of Consumer Information Search on Communication Product Choice in a Multi-channel Shopping Environment Using TAM Model

In the era of ubiquitous technology and information accessibility, multi-channel shopping has emerged as a prevalent consumer purchasing mode. This diversified shopping landscape presents consumers with an array of shopping channels and product options, profoundly influencing their shopping behaviors and decision-making processes, particularly in the realm of information search. This study delves into the impact of consumer information search behavior on communication product choice within a multi-channel shopping environment, employing the Technology Acceptance Model (TAM) as the theoretical framework. The findings unveil a significant correlation between consumers' acceptance of various shopping channels and their information search behavior, which in turn shapes their communication product choices. Notably, consumers' perceptions of ease of use, information richness, and transaction security associated with shopping channels influence their channel acceptance, subsequently impacting their communication product choice behavior. These insights hold valuable implications for companies in crafting more effective marketing strategies and promotional plans, while also contributing to academic discourse on consumer behavior and the application of the Technology Acceptance Model.

E-commerce applications: A study of customers’ behavioral intentions and behaviors of using these applications towards online shopping

This study uses the UTAUT2 (Unified theory of acceptance and use of technology) model as well as adding other factors such as Platform Usability, User Autonomy to determine the behavioral intention and behavior of online shoppers using e-commerce applications (ECAs) in Vietnam. Using the analysis results from structural equation modeling, it was shown that Social Influence, Use Proficiency, Hedonic Motivation, User Skill, Effort Expectancy positively affect Behavioral Intention. At the same time, Behavioral Intention is negatively affected by Performance Expectancy. Behavioral Intention and Facilitating Conditions are two factors that positively affect Use Behavior. Besides, User Autonomy negatively affects Use Behavior. The research results are an important basis for ECAs providers, managers and stakeholders to apply in assessing the behavioral intentions and behaviors of online shopping customers using ECAs in Vietnam to promote the use of ECAs in online shopping.

Item response theory analysis of the Chinese version compulsive shopping scale

Compulsive shopping, a behavioral disorder with significant personal and social repercussions, necessitates reliable assessment tools, especially within different cultural contexts. While several scales exist to measure compulsive buying behavior, there is a lack of validated instruments tailored to the Chinese population. This study aimed to examine the psychometric properties of the Chinese version of the Compulsive Shopping Scale (CSS) using Item Response Theory (IRT). A total of 637 young healthy participants (42.5 % males and 57.5 % females), with a mean age of 21.32 years (SD = 2.06), both undergraduate and postgraduate students, were recruited from various cities in China. Sixty-four participants were retested after a two-week interval to assess test-retest reliability. The results indicated that a one-dimensional factor structure was appropriate. The reliability analyses, including test-retest reliability, ω, α, and λ6 tests, demonstrated good internal consistency. The rating scale model analysis showed infit and outfit MNSQ values between 0.6 and 1.4, indicating a good fit. The item information curve and test information curve indicated a normal distribution of scores. Differential item functioning was observed in two items, suggesting potential gender-related differences. These findings indicate that the Chinese version of the CSS is a reliable and valid instrument for measuring compulsive shopping behaviors in Chinese populations.

Pengaruh Trend Halal Lifestyle dan Gaya Hidup Terhadap Perilaku Belanja Mahasiswa

This study explores the impact of the halal lifestyle trend and lifestyle on the shopping behavior of students at the Faculty of Economics and Islamic Business (FEBI), UIN SYAHADA Padangsidimpuan. The research employs a quantitative approach, utilizing both primary and secondary data sources. Data was collected through the distribution of questionnaires to 96 respondents, and the analysis was carried out using SPSS version 23. The results of the study, as determined through partial (t-test) and simultaneous (F-test) analyses, reveal that the halal lifestyle trend does not significantly affect students' shopping behavior. On the other hand, the lifestyle variable demonstrates a positive and significant influence on students' shopping behavior. The combined effect of both the halal lifestyle trend and lifestyle is found to be positively and significantly correlated with the shopping behavior of FEBI UIN SYAHADA Padangsidimpuan students. In conclusion, the presence of the halal lifestyle trend does not result in an increase in students' expenditures or shopping behavior. However, lifestyle choices significantly impact students' shopping behavior, with higher lifestyle preferences leading to increased shopping activities.

Determinants of customer buying behaviour in omnichannel retailing: a systematic literature review and future research directions

PurposeAdvances in digital technologies and the growing number of touch points have had a significant impact on the shopping behaviour of omnichannel customers. Several research papers have explored different facets of omnichannel, but only a few have thoroughly explored the literature on showrooming and webrooming simultaneously. This paper aims to identify the key groups of antecedents influencing customer buying behaviour in omnichannel, under the influence of digital technologies, with a particular focus on showrooming and webrooming.Design/methodology/approachOur study conducted a systematic literature review to identify the factors influencing customers’ buying behaviour in omnichannel, which have been the subject of academic discussion over the last decade. We finalized 149 articles for the thematic analysis and identified three groups of antecedents: channel-related, product-related and consumer-related with their subgroups.FindingsUnder channel-related antecedents, cost and perceived benefits, search convenience, need for interaction and situational circumstances have been identified as major attributes. The expressiveness of the product, product demonstration and search and experienced products have been identified under product-related antecedents, followed by price consciousness, past experiences, perceived risks and shopping motivations as leading attributes under consumer-related antecedents. The study revealed the multifaceted influence of digital technologies on omnichannel buying behaviour. Digital technologies are shaping the antecedents related to channels, products and consumers. Digital technologies simultaneously mediate between antecedents and the selection of a specific path within an omnichannel environment. Showrooming and webrooming should no longer be seen as general concepts. The rise of digital technologies has led to the development of new consumer journey patterns and the blurring of distinctions between showrooming and webrooming. A conceptual framework has been proposed to understand consumers' omnichannel behaviour, having considered the identified antecedents and the role of digital technologies.Practical implicationsThis study advances the academic understanding of consumer behaviour in omnichannel under the influence of digital technologies and provides important implications for omnichannel management. With the advancement of digital technologies such as augmented reality and virtual reality, retailers should implement channel integration strategies to bridge the gap between online and offline channels, providing a memorable shopping experience for omnichannel customers.Originality/valueThis study is unique because it identifies and analyses the antecedents of consumer behaviour in omnichannel settings under the influence of digital technologies. It also uncovers new potential combinations of showrooming and webrooming patterns. The proposed framework can help retailers in their future planning of omnichannel strategies.

Livestream sales prediction based on an interpretable deep-learning model

Although live streaming is indispensable, live-streaming e-business requires accurate and timely sales-volume prediction to ensure a healthy supply–demand balance for companies. Practically, because various factors can significantly impact sales results, the development of a powerful, interpretable model is crucial for accurate sales prediction. In this study, we propose SaleNet, a deep-learning model designed for sales-volume prediction. Our model achieved correct prediction results on our private, real operating data. The mean absolute percentage error (MAPE) of our model’s performance fell as low as 11.47% for a + 1.5-days forecast. Even for a 1-week forecast (+ 6 days), the MAPE was only 19.79%, meeting actual business needs and practical requirements. Notably, our model demonstrated robust interpretability, as evidenced by the feature contribution results which are consistent with prevailing research findings and industry expertise. Our findings provided a theoretical foundation for predicting shopping behavior in live-broadcast e-commerce and offered valuable insights for designing live-broadcast content and optimizing the user experience.