Influencing of Online Compulsive Buying and Materialism in Health and Beauty Consequence New Normal Shopping

The purpose of this study is to determine the impact of online compulsive buying and materialism on new normal shopping in the health and beauty industry. Depression, anxiety, stress, self-esteem, social media advertising, celebrity endorsement, consumerism, and compulsive shopping are all examined in this study. The samples (415 respondents) were drawn from an online questionnaire using probability-sampling procedures that included stratified and basic random sampling. After collecting the data, it was analyzed using simple and multiple linear regression to confirm and demonstrate the hypotheses' relevance. Multiple and simple linear regression analyses, as well as a five-point Likert scale analysis, were used to analyze the data. This study discovered that social media advertising and celebrity endorsement had a substantial effect on materialism, whereas stress, depression, low self-esteem, and anxiety have a significant effect on online compulsive buying. According to simple linear regression, materialism has a large impact on obsessive online shopping for health and beauty products in the new normal. Additionally, this study proposes that in order to acquire a better knowledge of compulsive buying behavior on online shopping platforms, researchers should examine a diverse group of respondents, such as elderly buyers, as well as other service industries. Attaining these objectives is highly likely to maintain compulsive online shopping behavior in a new typical scenario. The research paper's weaknesses include its narrow emphasis on Thailand and Thai customers. As a result, the conclusions from this research may not be applicable to other nations and will solely reflect the situation in Thailand

Social shopping for fashion

In spite of the significance of social shopping in the context of fashion consumption, its definitions, boundaries, and explanations have not yet been systematically established in literature. The purpose of Study 1 was to develop a reliable and valid scale of social shopping for fashion. With the scale, Study 2 aimed to develop and test a structural model of social shopping process. In Study 1, a three-step procedure for scale development was followed: item generation, scale purification, and scale validation. As a result, a five-dimensional scale, along with sixteen behavioral items, was developed representing distinctive dimension of social shopping for fashion. The result suggests that social shopping for fashion involves dynamic and complex direct/indirect interpersonal exchanges and activities. Study 1 adds significant value to the literature in three ways. First, the scale is the first attempt to synthesize dispersed concepts of social shopping. Second, by providing a reliable and valid measure of social shopping for fashion, the results advance the area of research. Third, the scale is useful for a wide range of marketing and retailing applications. In Study 2, an online survey was conducted with a random sample consisting of a total of 5,280 undergraduates aged 18 to 29 years old enrolled at a large southeastern university. A total of 858 responses were analyzed using structural equation modeling. A structural model including motivational forces and consequences of social shopping behavior was developed and tested. The results indicated that social comparison orientations were generally found to be motivators of social shopping for fashion, and social shopping contributed to shopping satisfaction. The results, however, suggest that each dimension is driven by different dimensions of social comparison orientation and generates different types of satisfaction. This study increases the understanding of social shopping by simultaneously examining a causal model depicting comprehensive motivational forces and consequences of social shopping behavior. The results contribute to building a rigor of social comparison theory and consumer satisfaction theory in the context of fashion consumption. The results also provide industry professionals with strategic cues for creation of shopping environments wherein consumers’ social needs are better served and satisfied

AN INTEGRATED MODEL OF FACTORS AFFECTING WEBSITE ADOPTION, PERCEIVED RISK AND TRUST ON ONLINE SHOPPING INTENTION IN CHINA

With the growth of e-commerce platforms, more and more customers are changing their shopping intention from physical stores to online platforms. China has a substantial population in the world and also has the completed e-commerce platform. Due to Covid-19 Pandemic, many consumers changed their behavior to be online shopping. E-commerce still has a large potential market in near future. Therefore, this research aims to test the influence of website adoption, perceived risk, and trust on online shopping intention. The researchers collected the data from online shoppers who bought products service from one of the most famous online shopping websites in China. The sample of this study was collected from 400 respondents through online. Non-probability sampling methods including purposive and convenience sampling  was applied to collect the data from the sampling units. The five-point Likert scale was designed for research instruments. Descriptive analysis and inferential analysis were applied to analyze the data and multiple linear regression analysis was applied to test all hypotheses. Based on the findings, the researchers found that perceived usefulness, perceived ease of use, social influence, and facilitating conditions significantly influenced online shopping intention. Perceived risk and trust also had a significant influence on online shopping intention

Online Shopping: The Influence of Body Image, Personality, and Social Anxiety

The usage of the Internet has experienced significant growth over the past several decades, providing a vehicle for the online shopping market to experience exponential gains as well. In a 2011 U.S. Census survey, 71.7% of households reported access to the Internet, an increase from the reported 54.7% in 2003 and furthermore a large increase from the 18.0% from the 1997, the first year the Census Bureau reported Internet usage (File, 2013). Research has shown various reasons for expansion of online shopping, such as convenience, ease, and the excitement of experiencing something new, but gives little insight into characteristics that lead consumers to choose to shop online (Huang & Yang, 2010; To, Lio, & Lin, 2007; Yen, Yen, Chen, Wang, Chang, & Ko, 2012). The consumer characteristics that lead to online shopping could be implications of body image dissatisfaction and social anxiety. Personality factors could also influence consumer’s decision to shop online rather than in store. Using a demographic group likely to shop online and a survey measuring body image satisfaction, personality and social anxiety, this study hypothesized these variables would predict online shopping behavior. Online shopping was measured by a questionnaire adapted from previous research and measures frequency and preference of online shopping. The Multidimensional Body-Self Relations Questionnaire (MBSRQ) was used to measure body image dissatisfaction; participants answered statements such as, “I like the way my clothes fit me” and “I am physically unattractive.” Social anxiety was assessed using the Social Interaction Anxiety Scale (SIAS). This measurement of fears of social interactions uses statements like, “I find it easy to make friends of my own age” and “I feel I’ll say something embarrassing when talking.” Finally, personality was evaluated by using the NEO Five-Factor Inventory- 3 (NEO-FFI-3), which measures the well-known Big Five personality constructs. Participants rated responses to statements such as, “I rarely feel lonely or blue” and “I like to be where the action is.” Sex differences in online shopping preference were also assessed. To analyze the data, a multiple regression was used to test the predictability of online shopping. Although the overall regression model was not significant, some correlations between variables were found. Social anxiety was significantly correlated with online shopping. Neuroticism was significantly correlated with online shopping. Body image satisfaction was significantly correlated with social anxiety, conscientiousness, extraversion, and neuroticism. Significant correlations were found between social anxiety, and consciousness, extraversion, and neuroticism. Agreeableness was significantly correlated with conscientiousness and neuroticism. Conscientiousness was significantly correlated with extraversion and neuroticism. A significant correlation was found between extraversion and neuroticism. To assess sex differences, an independent t test was used. It was found women shop online more frequently than men. The possible implications of this study can be far reaching and provide valuable information to many different fields. Clinicians will be better able to understand how body image issues and social anxiety affect client’s everyday life. The findings of the relationship between online shopping and consumer characteristics will help in understanding the underlying issues of those suffering from online shopping addiction or problems. This study assists in providing a complete picture of clients struggling with any of these issues, which, in turn, benefits the therapeutic process and allows for a holistic approach. Online retailers will be able to use the information yielded from this research to better target their intended population. Limitations include only using a population in a rural area, and restrictions of the shopping experience scale used. Future directions include using a diverse population, possibly in an urban area. This study aimed to understand online shopping behaviors by examining personality traits of online shoppers. This study adds to the literature on consumer characteristics of those who shop online

Problematic shopping behavior: an item response theory examination of the seven-item bergen shopping addiction scale

There has been an increasing amount of research examining problematic shopping behavior (PSB), often referred to in the psychological literature as “compulsive buying” or “shopping addiction.” A popular scale for assessing the risk of PSB is the seven-item Bergen Shopping Addiction Scale (BSAS). To expand our knowledge of the psychometric properties of this instrument, the present study employed Item Response Theory (IRT) and differential item functioning analyses (DIF) while concurrently attempting to determine a preliminary cut-off point. A relatively large community sample completed the BSAS online (N = 968, Mage = 29.5 years, SDage = 9.36, 32.5% women). IRT analyses showed differences regarding the BSAS items’ discrimination, difficulty, and precision, with a raw score exceeding 23 (out of 28) indicating a higher risk of shopping addiction. Finally, while most BSAS items operated equally among males and females, Item 2 (mood modification) required a higher level of shopping addiction behaviors to be endorsed by males. The BSAS functions as a reliable assessment of the risk of shopping addiction, particularly between average and high levels of the trait. Clinical implications are discussed in light of these findings

User-oriented recommender systems in retail

User satisfaction is considered a key objective for all service provider platforms, regardless of the nature of the service, encompassing domains such as media, entertainment, retail, and information. While the goal of satisfying users is the same across different domains and services, considering domain-specific characteristics is of paramount importance to ensure users have a positive experience with a given system. User interaction data with a system is one of the main sources of data that facilitates achieving this goal. In this thesis, we investigate how to learn from domain-specific user interactions. We focus on recommendation as our main task, and retail as our main domain. We further explore the finance domain and the demand forecasting task as additional directions to understand whether our methodology and findings generalize to other tasks and domains. The research in this thesis is organized around the following dimensions: 1) Characteristics of multi-channel retail: we consider a retail setting where interaction data comes from both digital (i.e., online) and in-store (i.e., offline) shopping; 2) From user behavior to recommendation: we conduct extensive descriptive studies on user interaction log datasets that inform the design of recommender systems in two domains, retail and finance. Our key contributions in characterizing multi-channel retail are two-fold. First, we propose a neural model that makes use of sales in multiple shopping channels in order to improve the performance of demand forecasting in a target channel. Second, we provide the first study of user behavior in a multi-channel retail setting, which results in insights about the channel-specific properties of user behavior, and their effects on the performance of recommender systems. We make three main contributions in designing user-oriented recommender systems. First, we provide a large-scale user behavior study in the finance domain, targeted at understanding financial information seeking behavior in user interactions with company filings. We then propose domain-specific user-oriented filing recommender systems that are informed by the findings of the user behavior analysis. Second, we analyze repurchasing behavior in retail, specifically in the grocery shopping domain. We then propose a repeat consumption-aware neural recommender for this domain. Third, we focus on scalable recommendation in retail and propose an efficient recommender system that explicitly models users' personal preferences that are reflected in their purchasing history

User-oriented recommender systems in retail

User satisfaction is considered a key objective for all service provider platforms, regardless of the nature of the service, encompassing domains such as media, entertainment, retail, and information. While the goal of satisfying users is the same across different domains and services, considering domain-specific characteristics is of paramount importance to ensure users have a positive experience with a given system. User interaction data with a system is one of the main sources of data that facilitates achieving this goal. In this thesis, we investigate how to learn from domain-specific user interactions. We focus on recommendation as our main task, and retail as our main domain. We further explore the finance domain and the demand forecasting task as additional directions to understand whether our methodology and findings generalize to other tasks and domains. The research in this thesis is organized around the following dimensions: 1) Characteristics of multi-channel retail: we consider a retail setting where interaction data comes from both digital (i.e., online) and in-store (i.e., offline) shopping; 2) From user behavior to recommendation: we conduct extensive descriptive studies on user interaction log datasets that inform the design of recommender systems in two domains, retail and finance. Our key contributions in characterizing multi-channel retail are two-fold. First, we propose a neural model that makes use of sales in multiple shopping channels in order to improve the performance of demand forecasting in a target channel. Second, we provide the first study of user behavior in a multi-channel retail setting, which results in insights about the channel-specific properties of user behavior, and their effects on the performance of recommender systems. We make three main contributions in designing user-oriented recommender systems. First, we provide a large-scale user behavior study in the finance domain, targeted at understanding financial information seeking behavior in user interactions with company filings. We then propose domain-specific user-oriented filing recommender systems that are informed by the findings of the user behavior analysis. Second, we analyze repurchasing behavior in retail, specifically in the grocery shopping domain. We then propose a repeat consumption-aware neural recommender for this domain. Third, we focus on scalable recommendation in retail and propose an efficient recommender system that explicitly models users' personal preferences that are reflected in their purchasing history