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Personal data and information collected online by companies can be used to design and personalise advisements. This chapter extends existing research into the online behavioural advertising by proposing a model that incorporates artificial intelligence and machine learning into developing emotionally appealing advertisements. It is proposed that big data and consumer analytics collected through AI from different sources, will be aggregated to have a better understanding of consumers as individuals. Personalised emotionally appealing advertisements will be created with this information and shared digitally using pragmatic advertising strategies. Theoretically, this chapter contributes towards the use of emerging technologies such as AI and Machine Learning for Digital Marketing, big data acquisition, management and analytics and its impact on advertising effectiveness. With customer analytics making up a more significant part of big data use in sales and marketing and GDPR ensures data are legitimately collected and processed, there are practical implications for Managers as well. Acknowledging that this is a conceptual model, the critical challenges are presented. This is open for future research and development both from academic, digital marketing practitioners and computer scientist.

The purpose of this study is to empirically test a model that examines the roles of offline activities and customer value creation on tourists' continuance use of online travel communities (OTCs). Hypotheses were tested through a sample of 251 respondents on Amazon Mechanical Turk (MTurk). SmartPLS structural equation modeling was used to test the structural model. Results indicated that offline activities significantly influence hedonic and social values, while this support was not found with functional value. Similarly, while offline activities positively influence continuance usage intention, no positive relationship was established between offline activities and recommendation intention. Additionally, the three dimensions of customer value creation positively influenced continuance usage intention. This study suggests that in planning offline activities, managers of OTCs must understand the dynamics of customer value creation in order to enhance social bonds among members and continuous usage of the OTC.

Online travel reviews are paramount to trip planning because they help consumers' form images of destinations. Despite ample studies on hotel service attributes, knowledge is scarce regarding culturally nuanced attributes, including security perceptions. This study examines consumers' perceptions of service attributes and security/safety concerns of hotels in Africa. Data were extracted from three hotel categories (3, 4, and 5-stars), which were based on TripAdvisor rankings from Egypt, Ghana, Kenya, Nigeria, and South Africa. A hybrid analysis revealed that hotel service attributes and security/safety are cardinal evaluation criteria for visitors to Africa. Additionally, our study reveals that most negative reviews were from 5-star hotels and Egypt received more positive reviews in all the service attributes than other countries.

The aim of this study was to develop and test a model that examined the interactions among the customer value framework, recommendation intention and customer characteristics in an online travel community (OTC). Data were obtained using Amazon Mechanical Turk from 251 members of an OTC as a sample. The partial least squares method was used to analyse the data. We found that all the variables of the customer value framework, including functional value, hedonic value and social value, were positively related to recommendation intention. In addition, using multi-group analyses, the study found differences between how different customer segments perceive each of the value dimensions and their effect on recommendation intention. Theoretical and managerial implications are offered.