The data-driven economy is transforming with data centers becoming a crucial business infrastructure. However, the increasing reliance on data centers is posing a threat to the environment. Climate change activists are focusing on reducing emissions from sectors like automotive, aviation, and energy. Data centers consume more electricity than the UK, accounting for 3% of global electricity supply and 2% of total greenhouse gas emissions. By 2040, digital data storage is projected to contribute to 14% of the world's emissions. The number of data centers worldwide has surged from 500,000 in 2012 to over 8 million, with energy consumption doubling every four years. The rise in internet penetration rates and the introduction of 5G technologies and IoT devices will further exacerbate the issue, increasing the demand for data processing.

The internet of things characterizes a unified structure of internet-supported objects that can gather and send data through a wireless network with no intervention of humans. By this structure, the present business world is experiencing remarkable changes due to the irresistible potential derivable from the internet of things. This technology is already bringing significant changes across the industries, of which the digital marketing sector has the maximum benefits. This technology collects various data from the consumers through different forms of digital marketing platforms such as social media marketing, online marketing, electronic mail marketing, pay-per-click advertising. With these types of data, marketers can generate some meaningful insights, develop interactions with customers, communicate with sellers and customers, and can also forecast the behavior and lifestyle of the customers. With this backdrop, the chapter has made an attempt to explain the relevance of the internet of things in digital marketing.

Blockchain technology ensures data security through an integrated system whereby it collects, arranges, stores, and disseminates information in different blocks. This technology thus enables adding the data to the network. Once data has been added to the network, no one can alter the data set either by adding or deleting it. Further, this technology also helps to track and check the changes if anything is made to a blockchain, as the changes remain in the database forever. Since this technology uses lots of systems in a blockchain, it will regularly download its data, arranging, and keeping the copy locally. Locating the data errors and cyberattacks in advance by analyzing the data documented, it employs the consent of various participants and accomplishments in cryptography. With this backdrop, the chapter has attempted to disclose the basics of blockchain technology in data security, why blockchain in cybersecurity, how it ensures cybersecurity, its benefits, its innovative uses, and the future of cybersecurity in the online business platforms.

This chapter delves into the complexities and recommendations associated with the management of electronic waste, paving the way for a transition towards a circular economy. It underscores the significance of adopting sustainable practices, including eco-friendly product designs, extended producer responsibility, recycling, and remanufacturing. Several challenges are identified, such as the scarcity of data regarding material concentrations in specific electronic equipment and the absence of universally applicable standard methods for evaluating elemental concentrations in e-waste. The chapter underscores the value of integrating technical support and financial incentives to bolster e-waste management efforts. Furthermore, it highlights the pivotal roles played by the private sector, non-governmental organizations, and government bodies in the effective implementation of these strategies. Collaboration between businesses, governments, and consumers is underscored as essential for establishing a robust circular economy.

To understand customer repurchase intention in online stores, the present study has evolved with three variables: customer trust, loyalty, and satisfaction. A theoretical model was also developed to examine customer repurchase intention towards online shopping. The observation unit of this research is online stores and the analytical unit is the customers who have been purchasing grocery products via online. A random sampling technique was used to select the respondents and who are easily accessible. The data was collected from 645 online customers in Seoul. The data was analyzed with a structural measurement equation model by using Partial Least Square 3.0. Outcomes of study showed that, customer satisfaction and trust were the significant predictors of customer repurchase intention. The effect of customer loyalty towards repurchase intention seems to be very low. Hence, online retailers have advised to concentrate more on the activities needed to increase customer loyalty and to purchase more.