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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.

The globalisation of markets, emerging concepts of sustainable development, and circular economy have defined the boundaries within which organisations must compete and address the needs of key stakeholders. As circumstances change, boundaries are often replaced by the relationships between companies and the communities they serve. Consequently, strategy has become a central aspect of sustainable leadership and the foundation for implementing strategic management in a dynamic system of relationships. Every company is born and grows within social and economic ecosystems. Drawing on the metaphor of biology, ecosystems are described as dynamic interconnections among various elements that influence and foster entrepreneurship. Interconnections between players (such as marketplaces, organisations, governments, and universities) create a flow of expertise, abilities, knowledge, experience, and tangible resources. Economic and social ecosystems involve various actors and components that continuously coexist and interact, leading to the creation of numerous mutual relationships. Consequently, it is crucial for managers to gain a comprehensive understanding of the internal and external environments. Various decision-making tools and strategies can be used to achieve this goal. These tools were developed to assist managers, researchers, and consultants in making informed decisions under complex scenarios. This chapter presents several decision-making strategies and tools, including the Boston Consulting Group (BCG) matrix, General Electric (GE) matrix, Balanced Scorecard (BSC), PEST, PESTEL analysis, and SWOT analysis.

Work–nonwork policies and practices provide support for employee well-being, as well as a competitive advantage that can help differentiate organizations. However, not all work–nonwork policies and practices are effective, utilized, or relevant. In this chapter, the authors introduce “organizational boundary management strategy” as a way to leverage these policies and practices, making them more widely adopted and more effective. Organizational boundary management strategy refers to how an organization as a whole tends to support workers’ work–nonwork boundaries (i.e., via segmentation, integration, or somewhere in between). Although boundary management has historically tended to focus on how individuals navigate distinctions between work and personal life, the authors extend boundary management to the organization to suggest how understanding and aligning the organization’s overall boundary management strategies can support worker well-being. To expound on this, the authors present a model suggesting how organizational boundary management can be used to support worker well-being.

Adapting to external and internal transformations is a difficult task that managers and scholars must face while attempting to keep their organisations alive and well-established. This chapter explores the various decision-making tools that can assist practitioners and scholars to improve their understanding of the external scenario to determine the contemporary appropriateness of these approaches for analysing the environment and their implications for various types of organisations. The chapter investigates the barriers and drivers of these methods and proposes existing alternative paradigms created by academics and practitioners to analyse and comprehend the context. It demonstrates how these decision-making tools can be implemented by providing examples and case studies.

In today’s dynamic business environment, it’s crucial for companies to employ robust strategies and decision-making tools to remain competitive. This chapter explains the key comprehensive policies that can be applied by any company to maximise the effectiveness of the APPNIE framework. These policies include the following key elements for effective use of the APPNIE model: appointing an APPNIE manager responsible for maintaining and updating the model, collaborating with top executives, and formulating action plans; raising awareness of the APPNIE manager’s role across the organisation to encourage sharing valuable information (establishing clear procedures and channels for data collection); encouraging the communication of valuable data by offering recognition or rewards, ensuring a steady flow of filtered information; monitoring the eight quadrants using qualitative and quantitative data for up-to-date assessments of key APPNIE’s factors; organising regular meetings between APPNIE managers and directors from different functions to share perspectives, discuss action plans, and address challenges and opportunities. The chapter shows that by adopting these practices, companies can navigate ‘complexity’, make informed decisions, and enhance their overall success and competitiveness in the market.

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.

Senior travelers are an attractive market segment and increasingly participate in an internet-mediated society. This chapter examines the profile of senior tourists who booked their trip to the Azores using online travel agencies. The purpose is to analyze their motivation factors based on the travel motivation scale for senior tourists, using 17 items related to the motivations for visiting the destination, and to ascertain whether there are different groups of senior tourists based on sociodemographic characteristics, travel motivations, and experiences performed. Using a structured questionnaire and a sample of senior tourists, three distinct clusters are obtained.

In recent years, entrepreneurship education has become increasingly popular to promote economic development and encourage entrepreneurship. This chapter aims to review the effectiveness of entrepreneurship education and the mode of delivery. This review will help understand the effectiveness of entrepreneurship education (entrepreneurial skills, entrepreneurial intention, enhanced entrepreneurial mindset and greater business success). It will also help understand the mode of delivery (classroom instruction, experiential learning, mentoring and coaching and online learning) Using the traditional review methodology, the chapter focusses on the outcomes of entrepreneurship education on students and their attitudes, intentions and behaviours towards entrepreneurship. The chapter also evaluates the various modes of delivery, including traditional classroom-based instruction, experiential learning and online education. The review compared the United States of America and South African points of view on entrepreneurship education to posit significant directions on how to effectuate entrepreneurship education. The chapter further reviews the sustainable development goals that are aligned with entrepreneurship. Overall, the chapter concludes that entrepreneurship education is effective in promoting entrepreneurial attitudes, intentions and behaviours, and its mode of delivery significantly influences its impact. The chapter recommends the integration of active and experiential learning methods into entrepreneurship education, and the development of innovative modes of delivery to reach a wider audience. Policymakers should also support the integration of entrepreneurship education into the formal education system to promote economic development.

Introduction: In today’s world of increasing competition, diminishing product differentiation, higher customer expectations, easy product replacements and lowering brand loyalty, organisations are evolving new marketing strategies for economic, societal and sustainability. Cause-related marketing (hereafter referred to as CRM), a strategic sustainable philanthropic practice, is the upcoming form of CSR. CRM plays an instrumental role in achieving self-brand connection and brand loyalty.

Purpose: To explore, integrate and interconnect concepts of CRM and self-brand connection to get more insights into the imperative role of CRM strategy in developing self-brand connections that can lead to brand loyalty in the most sustainable way. For this, CRM and self-brand connection, as proposed by societal marketing and branding literature, were explored. This chapter is a propositional inventory where the researcher has explored the antecedents of CRM strategy and its role in developing brand loyalty through self-brand connection.

Methodology: This chapter is centred upon the existing literature on sustainability, CRM and branding to understand better the relationships between dimensions and consequences of CRM and its interlinkage with brand loyalty.

Findings: The literature recommends that selected dimensions: Cause-brand fit, product type, altruistic motivation and brand credibility determine the effectiveness of CRM strategy. It also establishes the profound impact of attitude towards brand, brand perception and brand distinctiveness on self-brand connection. A theoretical framework based on the existing literature represents an amalgamated groundwork for developing effective, sustainable CRM strategies in conjunction with the self-brand connection. The proposed framework is distinct as no study conjoins the abovementioned concepts and aims to comprehend whether this integration is brand loyalty.

The smart destination can be defined as a destination that deploys information communications technologies (ICTs) and other technological tools for interactive/participative engagement with prospective visitors. The rationale is to enhance the residents' quality of life as well as the tourists' experiences of the destinations they visit. This chapter discusses about digitization strategies and on marketing superstructures affecting destinations. It advances a conceptual framework through the development of an Attracting, Stay, and Return (ASR) Model that is suitable for smart destinations. It relies on descriptive case studies to conceptualize smart tourism destinations. This contribution reiterates the importance of having a well-designed website that presents appropriate content to entice the prospective travelers' curiosity about destinations. It offers valuable insights and advances new knowledge on smart marketing approaches that are intended to increase the destination management organizations' outreach with tourists at each stage of the ASR Model.