Mansi Panwar

Tourism is one of the industries most thoroughly reorganised by digital platforms. Online travel agents, accommodation marketplaces, ride-hailing networks, experience platforms, and workforce-matching apps now intermediate much of what used to be handled inside hotels, agencies, and tour companies. Artificial intelligence is the connective tissue of this ecosystem, powering matching, pricing, ranking, moderation, and workforce coordination across its many sides. This chapter examines platform strategy and the AI ecosystem in tourism markets through a human resource management (HRM) lens. Drawing on platform and ecosystem theory, the chapter argues that HRM in tourism platform ecosystems must be redesigned around three realities: the workforce now spans employee, partner, and platform-worker categories; AI increasingly mediates the employment relationship; and the employer brand is co-produced with the platform brand. It maps HRM across the platform lifecycle, analyses five HRM dimensions, and concludes with ethics, governance, and a research agenda.

The integration of Environmental, Social, and Governance (ESG) practices in family firms is gaining importance, yet research in this domain remains theoretically fragmented and empirically inconclusive. This study presents a multi‐method systematic review of 73 peer‐reviewed articles, integrating bibliometric analysis with the Theory, Context, Characteristics, and Methodology (TCCM) framework and a nomological network to synthesise the intellectual landscape of ESG research in family firms. Five thematic clusters are identified‐corporate governance, socioemotional wealth, ESG disclosure, sustainability performance, financial performance, and ownership composition‐reflecting a broader disciplinary shift from governance‐centric to sustainability‐oriented inquiry. Socioemotional wealth and stewardship theory emerge as the dominant theoretical lenses explaining ESG adoption in family firms. The findings carry meaningful implications for investors, policymakers, and family firm executives seeking to align ownership values with contemporary sustainability demands. This study offers the first comprehensive synthesis of this intersection and charts a structured agenda for future research.

Financial forecasting in travel and tourism organizations has undergone a dramatic technological transformation with the widespread adoption of artificial intelligence and machine learning tools. Yet the persistent underperformance of forecast-driven financial plans in the sector especially during demand discontinuities such as the COVID-19 pandemic, geopolitical disruptions, and fuel price shocks raises a question that purely technical explanations cannot fully address: are the cognitive biases of the human actors who commission, interpret, and act on AI-generated forecasts systematically corrupting the quality of financial decisions? This chapter answers that question in the affirmative.

The fact that automation technologies and robots systems have been incorporated into the operations of travel services is fundamentally reorganizing the cost structures, revenue structures as well as the financial governance structures of businesses that are involved in the global travel and tourism sector. In this chapter, the author explores the financial aspects of automation and robotics adoption in the travel services operation, which includes airlines, hotels, airports, and the corporate travel management functions. The chapter builds its argument based on institutional economics, the capital investment theory, and financial management literature, establishing a taxonomy of automation modalities, which includes robotic process automation (RPA), physical service robotics, and cognitive automation driven by artificial intelligence and examines its financial impact on the reduction of costs, revenue growth, and risk management.

The world financial system is undergoing a structural change that is influenced by the merging of the technological advancement and the geopolitical restructuring. The accelerated development of artificial intelligence (AI) and the increased use of economic sanctions as a tool of statecraft are among the most powerful forces that contribute to this change. These forces are collectively reshaping the dynamics of financial markets, changing the risk transmission processes, and making the strategic environment of firms in cross-border settings more complex.

Switching to a low-carbon economy will demand significant funding of environmentally-friendly investments. Nevertheless, commonly traditional financial systems are known to experience problems like high transaction costs, information asymmetry and less transparency which limits the efficient mobilization of green capital. The chapter discusses the potential of financial technology (FinTech) to revolutionize the green credit market and institutional channels of carbon reduction. The conceptual and analytical approach incorporating the results of the literature on sustainable finance, digital financial ecosystem, and climate policy, the chapter examines how digital lending platforms, blockchain-based verification, artificial intelligence-based credit evaluation, and data-driven environmental monitoring can improve the effectiveness and reliability of the green finance.

Cryptocurrencies have also seen their development within the last decade becoming a globally popular financial phenomenon that once existed as a niche technological experiment. What started with the launch of the bitcoin in 2009 has grown into a massive ecosystem of digital assets, decentralized applications and blockchain-based financial services (Zribi et al., 2023). These inventions have been a paradigm shift in the conventional concept of money, trust and the financial intermediation. Cryptocurrencies have provided new avenues in financial inclusion, especially in areas whose banking systems are poorly developed, due to the ability to conduct peer-to-peer transactions that do not require centralized institutions, like banks or governments (Sapra & Shaikh, 203).

With the advent of Industry 5.0, focus has moved from technology to human-centricity, sustainability and well-being of society. The chapter explores how human-centric Artificial Intelligence (AI) contributes to the sustainable consumption behavior by enabling personalized recommendations, building trust, raising environmental awareness, and optimizing resources. Based on empirical research published over the last few years, the chapter discusses the main mechanisms by which AI can impact consumer decision making and outlines examples of its use in circular economy platforms, sustainable product choice, education and resource management. It also addresses issues of privacy, transparency, accessibility and environmental issues and proposes future research directions for harnessing AI to promote sustainable consumption and sustainable development goals.

Brands are becoming more judged by modern consumers based on the ethics, sustainability and social responsibility. The key to this change is the idea of cause marketing, which means such strategic partnerships between companies and social or environmental causes. Cause marketing unlike the traditional philanthropy which is usually conducted as a peripheral operation, incorporates social impact into the value proposition of the brand. It establishes a win-win situation: companies are better positioned to have their brand image, customer loyalty, and competitive markets differentiation, whereas social causes get finances, publicity, and increased exposure.