## 2 Literature Review and Research Design
### Psychographic Segmentation and Consumer Behavior  
Market segmentation consists of viewing a heterogeneous market as a number of smaller homogeneous markets, with differing product preferences among important market segments for the purpose of developing better firm strategies. Consumer behavior and predictive preference models can be much more effective when the target audience is split into different customer segments and individually tailored predictive models are developed for each segment [32], with some of them based on psychographics. Researchers have based the psychographic segmentation of markets on various factors, including personality, lifestyle, and values. Early psychographics mainly considered personality, but research based on one of the personality profiles, that is, Edwards Personal Preference Schedule, failed in predicting consumer purchase decisions. Other scholars used the BFF personality model. [PERSON], [PERSON], and [PERSON] (2012) surveyed people about various marketing messages and found that people respond more positively to messages tailored to their personality [33]. In the music domain, [PERSON] et al. (2016) showed that online music recommendations are more successful  
Figure 1: Schwartz Value Survey circumplex. when they leverage the correlations between people's personality and their music preferences [34]. Similarly, [PERSON], [PERSON], and [PERSON] (2016) discovered correlations between personality and movie preferences among Netflix users [35]. Additionally, studies in marketing have found that personality explains only a surprisingly small amount of the overall variance in consumer behavior [11]. Various studies have been conducted on the effect of user lifestyles on consumers' products purchases [36] and preferences [37; 38]. The comprehensive results have confirmed the effect of consumer lifestyle in predicting users' online preferences. [PERSON] et al. (2009) assessed consumer lifestyles regarding the adoption of electronic products, and the results based on structural equation modeling demonstrated that consumer lifestyle factors (fashion consciousness, leisure orientation, internet involvement, and e-shopping preferences) are direct and indirect antecedents of consumers' intentions to adopt high-tech products [36]. Based on the AIO survey, [PERSON] et al. (2017) investigated the relationship between lifestyle and online user preferences, and found that there are weak monotonic correlations between Facebook likes and the lifestyles of individuals [38]. Based on a web-usage lifestyle scale developed by AIO, [PERSON] et al. (2014) applied a tagging method to identify online lifestyles and to make recommendations according to the similarity of a user lifestyle tag to a telecommunications company product lifestyle tag [37]. In addition to lifestyle and personality, the relationship between value and consumer preferences has also been asserted. Scholars have used the LOV construct in relation to consumer fashion leadership [39], involvement [40], and brand preferences [41]. Based on questionnaire data, [PERSON] and [PERSON] (2013) investigated the effects of Malaysian consumers' personal values using mainly LOV measurements with regard to their overall behavioral intention and purchase satisfaction [42]. [PERSON] and [PERSON] (2006) adopted VALS and identified which values and lifestyles best explain environmentally friendly behaviors [43]. Based on RVS, [PERSON] and [PERSON] (2007) demonstrated that technology must be leveraged via links to customer values [44]. [PERSON], [PERSON], and [PERSON] (2009) constructed a luxury-related value for identifying different types of luxury consumers, and found that hedonic value aspects as components of the SVS of self-directed pleasure and life enrichment were most important for their perception of luxury value [45]. The combination of segmentation and prediction, where segmentation is used to help build segment-wise prediction models, has been a common segmentation approach and has been applied to different business scenarios.