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The versatility of customer relationship management (CRM) systems has kept these technologies popular over the years. These solutions have been integrated into organizations of all sizes, from large corporations to small- and medium-sized enterprises. Similarly, CRM systems have also found applications in all types of industries and business sectors. All this has been the driving force behind the proliferation of CRM solutions around the world. In this chapter, the author not only reflects on the impact and democratization of CRM systems on business management and marketing strategies but also explores how these technologies can determine the company's income. In particular, the author presents an experiment that analyzes the extent to which the volume of annual investment in CRM solutions can be used to predict annual net income in a sample of companies. Using time series analysis and applying the autoregressive integrated moving average modeling technique, the researcher examines a sample of 10 companies from different industries, and countries, over a 20-year period. The results show the efficiency of the predictive models developed in nine of the 10 companies analyzed. The findings of this study allow us to conclude that there seems to be an association between the investments made in CRM solutions and the income of the companies that invest in these technologies.

This chapter presents both main arguments of dividend policy theories and their empirical evidence. According to Miller and Modigliani (1961), dividend decisions are not relevant to firm value in a perfect capital market. Nevertheless, there are several market frictions in the real world (e.g., information asymmetry, agency problems, transaction costs, firm maturity, catering incentives and taxes). Therefore, academics use them to develop theories which help them explain corporate dividend decisions. Particularly, signaling theory considers dividend payments as a signal about firms' future prospects since outside investors face information disadvantage. “Bird-in-hand” theory argues that investors prefer dividends to capital gains since the former have lower risk than the latter. Agency theory is developed from the conflict of interest between corporate managers and shareholders. Corporate managers have high incentives to restrict dividend payments. Furthermore, transaction cost theory and pecking order theory posit that firms prefer internal to external funds. This drives firms to hold more cash and pay less dividends. Life cycle theory explains dividend policy by firm maturity. Mature firms have fewer investment opportunities, and thus, they tend to pay more dividends. Catering theory states that dividend decisions are based on investors' demand. Firms pay more dividends since investors prefer dividends and assign higher value to dividend payers. Tax clientele theory argues that firms that have corporate dividend policy rely on the comparative income tax rates for dividends and capital gains. Under the tax discriminations against dividends, firms tend to restrict their dividends in order to increase their stock prices.

Social media has become an indispensable part of modern politics. Its rise in the political arena has coincided with the decline in trust toward mainstream media. Today, more than half of the population gets their political news and information through social media platforms like Twitter and Facebook. Social media offers a great marketing opportunity to politicians as they allow them to bypass traditional media and communicate directly with voters, engage citizens during campaign and noncampaign periods, and create a brand image. As social media's influence in politics grows, so has the research devoted to political marketing on social media. It is against this backdrop that this chapter is written, which provides readers with an overview of the academic domain and the current state of literature. The chapter highlights the various research areas that have been explored in the literature and the implications of social media for political marketing strategy, along with the domain's current limitations and possible avenues of further research.

Knowledge management (KM) is an important topic in the age of big data, and this study adds to the existing body of literature by providing a novel KM perspective on the technological phenomenon of artificial intelligence (AI). This study aims to discover how AI might facilitate knowledge-based business-to-business (B2B) marketing. In this chapter, the authors take a close look at the building blocks of AI and the relationships between them. Future research directions and also the effects of the various market information building components on B2B marketing are discussed. The study’s approach is theoretical; it tries to provide a framework for characterising the phenomenon of AI and its constituent parts. Additionally, this chapter provides a methodical analysis of the three categories of market information crucial to B2B marketing: knowledge of customers, knowledge of users, and knowledge of external markets. This research looks at AI through the lens of the conventional data processing framework, analysing the six pillars upon which AI systems are founded. It also explained how the framework’s components work together to transform data into actionable information. In this chapter, the authors will look at how AI works and how it can benefit B2B knowledge-based marketing. It’s not aimed at AI experts but rather at general marketing managers. In this chapter, the possible effects of AI on B2B marketing are discussed using examples from the real world.

It is important to implement artificial intelligence (AI) because it can simplify and solve complex problems faster than humans. Because AI learns about people and their behavior from the first purchase, AI marketing can boost marketing efforts by leveraging data to target extremely precise consumer groups. There is a debate about the efficacy of AI marketing due to the constraints and limits imposed by the system's nature. This chapter presents insights from published studies regarding the relationship of AI with marketing and how AI can affect marketing. A real-world example of Netflix's usage of AI in marketing has been demonstrated. Then, consumer attitudes regarding AI were revealed. Then, several ethical considerations concerning AI were highlighted. Finally, the anticipated future of AI marketing was addressed. This chapter demonstrated the significance of firms implementing AI marketing to get a competitive advantage. Although some of the difficulties mentioned in this study need to be resolved, AI marketing has a bright future. There are ethical concerns about bias and privacy that should be addressed further. This chapter will encourage firms to use AI systems in marketing, and it will open the door to concerns that will need to be investigated academically in the future.

Buying online has become a widespread and common activity for consumers, and, for many organizations, e-commerce has become a very profitable alternative to sell their products and services, also allowing them to leverage their strategy in new geographical markets immediately. Although the literature on the subject is comprehensive, there is a gap in identifying the holistic constructs that are the determinants of consumers' choice of an online store. This research resorts to an exploratory study, based on a nonsystematic literature review, seeking to identify these constructs. The results obtained allowed us to identify the following constructs: consumer behavior, customer experience, web content, catalog, terms and conditions, customer support, perceived value, trust, security and privacy, satisfaction, and loyalty. Customer experience, satisfaction, and loyalty constructs stand out from a strategic perspective.

The management of consumer privacy has become a critical concern for organizations in the age of artificial intelligence–powered marketing. The impact of data on the market environment has brought both benefits and challenges, with marketers gaining valuable insights but also raising privacy concerns. As artificial intelligence–powered marketing advances, consumer vulnerability increases due to the sensitivity of collected data. This vulnerability leads some consumers to resort to falsifying information, posing a significant threat to the digital economy. Privacy empowerment and customer control play a vital role in addressing these challenges. This chapter explores the influencing factors and ethical considerations surrounding data falsification. It also discusses strategies to mitigate perceived vulnerability through privacy controls and explores the consequences of data breaches and customer vulnerability. The chapter further emphasizes the need for organizations to balance benefits, risks, and customer trust while harnessing the value of customer data. An ethical framework for data privacy marketing audits is proposed to help organizations assess their data practices responsibly and competitively. By integrating personal data protection strategies within an ethical framework, organizations can protect consumer privacy, enhance customer trust, and maintain their competitive edge in the market.