Using AI to personalise emotionally appealing advertisement

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 incorpo-rates 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 bet-ter understanding of consumers as individuals. Personalised emotionally ap-pealing advertisements will be created with this information and shared digi-tally using pragmatic advertising strategies. Theoretically, this chapter con-tributes towards the use of emerging technologies such as AI and Machine Learning for Digital Marketing, big data acquisition, management and analyt-ics and its impact on advertising effectiveness. With customer analytics mak-ing up a more significant part of big data use in sales and marketing and GDPR ensures data are legitimately collected and processed, there are practi-cal implications for Managers as well. Acknowledging that this is a concep-tual model, the critical challenges are presented. This is open for future re-search and development both from academic, digital marketing practitioners and computer scientist

Examining consumer behaviour in the UK energy sector through the sentimental and thematic analysis of tweets

Consumer engagement with brands on social media has been empirically proven. However, little is known about consumers' natural behaviour on social media, as literature on this topic is still in an early stage of its evolution. Accordingly, this study aims to investigate and understand the group interactions of consumer behaviour, with a specific focus on tweets within the UK energy sector. Energy is a significant utility in the United Kingdom, and the sector is evolving more rapidly than ever before, with pressure being applied to energy suppliers to meet the demands of consumers. This study draws on social capital theory to investigate how UK consumers engage with their suppliers, as well as the knowledge‐sharing capabilities of the Twitter community. In Study 1, Python was used to conduct tweet mining and sentiment analysis to investigate the polarity in consumer engagement with 82 energy companies in the United Kingdom. Results indicated overall positive sentiments towards the energy suppliers, although the level of engagement varies across the different groups of suppliers. Study 2 followed up with a qualitative insight into the factors shaping consumers' behaviour as they engage with brands on social media. A thematic model emerges in the form of an interrelated conceptual theory comprising three stakeholders, the key relationships between them, and their natural behaviours. This study offers a contemporary, essential, and interconnected understanding of consumer behaviour online with a focus on the energy sector, and further advances research into online consumer behaviour, sentiment analysis, netnography, and social media research

Customer value framework and recommendation intention: the moderating role of customer characteristics in an online travel community

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

Social media for universities’ strategic communication: how Nigerian universities use Facebook

A university has many stakeholders with varying interests and commitments. Several studies have examined modes and methods of HEIs communication with stakeholders. To the best of our knowledge, it is not evident in the literature how the engagement between universities and their stakeholders proceeds on the social media platforms particularly from a developing country perspective. This study employed stakeholder theory to give newer understanding to social media marketing as a strategy to reach university stakeholders and utilised an inductive, generic, qualitative approach in a netnography context to achieve the aim of this study. Theoretically, this chapter makes three key contributions. First, it extends the knowledge of the use of social media by universities, moving beyond the use of websites as strategic, interactive stakeholder engagement tools. Second, the study extends the application of stakeholder theory to include university conversations on social media, especially regarding the higher education institutions from a developing country perspective. Third, while acknowledging the unique and dynamic nature of stakeholders on social media, the study adopts a unique methodology to capture the usage of social media by the universities and explored their level of activity and analysed stakeholder responses. Methodologically, the study contributes to the literature on social media research

Influence of offline activities and customer value creation on online travel community continuance usage intention

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