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Due to climate change and global warming, sustainable consumption—as one possible solution to these challenges—is becoming more and more relevant. One generation that is affected the most by these developments are the millennials. While more and more baby boomers are retiring, millennials are now transitioning from being in training to being full-time employed, which marks a big change in their lives and makes understanding their values and consumption behaviour more important for marketers. The goal of our study is to clarify which values influence the building of attitude of millennials, if this influence differs according to employment status, and how attitude affects purchase intention concerning sustainable goods. Building to the list of values by Kahle (1983), the theory of planned behaviour, and perceived consumer effectiveness, we construct a framework to understand how values and employment status of millennials interact with their purchasing intention. Our results show that, among others, the values, that play a role during purchase intention forming, differ depending on the employment status. We also find that millennials place high importance on being in control when purchasing sustainable goods. Advertising and product managers can use our results to better understand and target the audience of their products as they construct their marketing efforts with the values of the audience in mind. In particular, messages that comply with the notion of being in control should be considered in every communication channel. This way, they may increase the share of sustainable consumers.

The increasing importance of online distribution channels is paralleled by a rising interest in gaining insights into the customer journey and browsing behavior. We evaluate several machine learning methods (latent Dirichlet allocation, correlated topic model, structural topic model, replicated softmax model) with respect to their ability to reproduce the browsing behavior of households across websites. In addition, we compare these machine learning methods to a related classical technique, singular value decomposition. In our study, the replicated softmax model outperforms latent Dirichlet allocation, but the correlated topic model attains the overall best performance. Compared to singular value decomposition both the correlated topic model and the replicated softmax model lead to a more efficient compression of web browsing data. On the other hand, singular value decomposition surpasses latent Dirichlet allocation. We interpret results of the correlated topic model and the replicated softmax model by determining combinations of topics or hidden variables that are heterogeneous with respect to visited websites. We show that decision makers should not rely on bivariate measures of site visits, as these do not agree with measures of interdependences between sites that can be inferred from the correlated topic model or the replicated softmax model. We investigate how well topics or hidden variables measured by these methods predict yearly household expenditures. The correlated topic model leads to the best predictive performance, followed by the replicated softmax model. We also discuss how the replicated softmax model can be used to support online marketing decisions of websites.

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