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This chapter describes how Astronaut and Aerospace Engineer, Jeanette Epps, dealt with the ambiguity of being pulled from a space flight by the National Aeronautics and Space Administration (NASA), leveraged emotional intelligence to adapt to the challenge, solidified valuable leadership lessons on resilience, courage, and vulnerability, and provides advice for female leaders on navigating the leadership labyrinth. Fundamentally, the hardship Epps experienced deepened her sense of purpose and leadership identity, making her an exceptional role model for female leaders everywhere.

Purpose — To provide a theoretical background to understand current local search engines as an aspect of specialized search, and understand the data sources and used technologies.

Design/methodology/approach — Selected local search engines are examined and compared toward their use of geographic information retrieval (GIR) technologies, data sources, available entity information, processing, and interfaces. An introduction to the field of GIR is given and its use in the selected systems is discussed.

Findings — All selected commercial local search engines utilize GIR technology in varying degrees for information preparation and presentation. It is also starting to be used in regular Web search. However, major differences can be found between the different search engines.

Research limitations/implications — This study is not exhaustive and only uses informal comparisons without definitive ranking. Due to the unavailability of hard data, informed guesses were made based on available public interfaces and literature.

Practical implications — A source of background information for understanding the results of local search engines, their provenance, and their potential.

Originality/value — An overview of GIR technology in the context of commercial search engines integrates research efforts and commercial systems and helps to understand both sides better.

Purpose: Many Internet users search for health information but they struggle with assessing the quality of the information they find. By drawing on a multi-modal approach to data collection, this study aims to understand further the nuanced cognitive processes that people utilize as they acquire and evaluate online health information.

Design: We used a mixed-methods approach that includes surveys, interviews, and observations of 76 diverse adults of all ages in the Chicago area completing various health information-seeking tasks.

Findings: Most participants begin their information-seeking process on search engines. We identified the most popular credibility-assessment strategies used on the search engine results’ pages (SERP) as well as on websites. We also explored how the process of executing such strategies reveals greater and lesser savvy among users.

Research Limitations: While the sample size and methods limit its generalizability, this study included a larger and more diverse group of participants than most observational work, which results in data about a wider range of behaviors than is typical of such research.

Social Implications: Our findings showed that most of our participants could use additional education regarding credibility assessment of online health information. Additionally, since a great deal of credibility assessment occurs on SERP, search companies bear a particular responsibility for ensuring the quality of the information their results highlight.

The impact of artificial intelligence (AI) and extended reality (XR, including virtual reality [VR], augmented reality [AR], and mixed reality [MR]) on marketing in Industry 5.0 and Society 5.0 is explored with systematic literature review in this chapter. AIXR is becoming a necessary aspect of marketing, driven by efficiency, productivity, and innovation. Despite AI's capabilities, the human touch in marketing is preferred due to superior adaptive, creative, and innovative abilities. The use of fully automated marketing systems is limited to specific tasks. This research will benefit both practitioners and academics focusing on AIXR in marketing and is limited by the number of included literature.

Applications powered by artificial intelligence (AI) and machine learning (ML) have become a crucial factor for success in modern sales organizations. This chapter investigates how Salesforce achieves scalable AI for businesses of all sizes and explores sales applications of AI and machine learning that are most common across industries. It is divided into three sections. The first section gives an introduction to AI and machine learning. The second section shows how data and automated machine learning models provide the foundation for AI applications and explains how Salesforce achieves scalable AI and machine learning for business applications. The third section demonstrates how AI applications impact the modern sales organization and the work of sales representatives. AI does not replace humans; it allows sales organizations to better engage with prospects and customers. Sales representatives using AI outperform their counterparts that rely purely on traditional methods.