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This chapter presents the outcome of a study that examined the phenomena ‘Is Tourism Regulation Catalyst for Abuja Federal Capital Territory (FCT) Nigeria, Hotels Business Performance Innovation?’ Previous studies on this subject area have been largely done around tourism and its impact on hotel business performance and the relationship between tourism regulation and hotel business performance from mostly western perspectives. Hence, this study aims to investigate the direct effect of tourism regulations on hotel business performance in Nigeria. Quantitative survey questionnaires were used to collect data from 176 participants comprising general managers and departmental heads in 22 key Abuja FCT Nigerian hotels. Balanced Scorecard (BSC) developed by Norton and Kaplan was adopted as the study’s theoretical framework. Data collected were analysed using the simple linear regression technique and Statistical Package for the Social Sciences (SPSS) statistical analysis software tool.

The result of the analysis shows that tourism regulation has a significant and positive correlation with Abuja hotel business performances based on the BSC four dimensions of financial, customer, processes and learning and growth. The unique city characteristics of Abuja FCT were also identified as an issue for consideration in future tourism regulation innovation by the regulatory authorities. This study contributes to business performance measurement literature from the Abuja FCT hotels, Nigerian perspective, and sets an agenda for the Nigerian tourism regulators, the Nigeria Tourism Development Corporation (NTDC) charged with diversifying the Nigerian economy revenue through tourism performance innovation. Also, a policy study into city characteristics classification as a way of innovating tourism regulations and hotels business performance is suggested.

Corporate misconduct carries significant social and economic costs, and therefore regulators and other stakeholders seek to deter it. Despite the significant costs and deterrence efforts, corporate misconduct is widespread and our understanding of it is limited. As argued in this chapter, one key reason for this is the lack of understanding of the benefits and penalties of misconduct for the companies and individuals involved, as well as the detection of such behavior. This chapter seeks to advance our understanding of corporate misconduct and builds on the rational choice model (RCM) – where the decision to engage in misconduct hinges on a calculation of the expected costs and benefit – and links it to research in organization theory and strategy. Specifically, it sets a research agenda at the intersection of organizational and strategic perspectives, to deepen our understanding of corporate misconduct and shed light on opportunities for empirical and theoretical research which can potentially aid in developing effective deterrence strategies.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).

Improving a startup’s ability to obtain funding is critical to the survival of the organization. Although existing studies have observed various biases in investment decisions, few have studied the neural mechanisms behind such behavioral observations. We propose to apply cutting-edge neuroscientific techniques to uncover the neural processes engaged during pitches by entrepreneurs to investors and to use this new knowledge to identify strategy artifacts promoting pitch success. We hypothesize that pitches are dynamically shaped by covert cognitive, emotional, and social processes, which are in turn influenced by tactical approach (story-telling vs dry facts), physical context (online vs in-person), and demographics (gender, ethnicity). The role of inter-brain synchrony (i.e., correlation of cortical activity between brains) – within the startup team or between the entrepreneurs and investors – in pitch outcomes remains unknown. By uncovering the covert processes that mediate pitch outcomes, we provide an evidence-based, scientific approach to improving pitch success.

The recent pandemic of Covid-19 has substantially changed people’s daily lives. They work and interact even more based on information and communication technologies (ICT). The use of new technologies and the interconnectivity specific to smart cities have intensified in the context of the pandemic. A significant part of the population works from home, participates in concerts and other remote social activities, organizes online parties, communicates virtually with friends and family, etc. These transformations required an extended and more stable infrastructure, significant investments in the development of software applications dedicated to remote activities (streaming, contact tracing, security, online ordering and delivery, telemedicine, etc.), in specific services (data storage and applications, electronic signature services, etc.) and the integration of subsystems used in smart cities. This chapter examines the role of SARS-CoV-2 pandemic in the acceleration of digital transformations in smart cities due to the need and desire to digitize communities and public administrations. It has become a top priority for both private and public companies from smart cities in the context created by the pandemic.

As the size of the population is growing and the capacity of the planet Earth is limited, human beings are searching for sustainable and technology-enabled solutions to support society, ecology and economy. One of the solutions has been developing smart sustainable cities. Smart sustainable cities are cities as systems, where their infrastructure, different subsystems and different functional domains are virtually connected to the information and communication technologies (ICT) and internet via sensors and devices and the Internet of Things (IoT), to collect and process real-time Big Data and make efficient, effective and sustainable solutions for a democratic and liveable city for its various stakeholders. This chapter explores the concepts and practices of sustainable smart cities across the globe and explores the use of technologies such as IoT, Blockchain technology and Cloud computing, etc. their challenges and then presents a view on business models for sustainable smart cities.

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.

This chapter of the book aims to achieve sustainability and productivity in light of the interaction between managers and engineers in a lean and agile supply chain management system in today’s organizations. The main innovation of this chapter is the use of the balanced scorecard (BSC) model and fuzzy analysis network process (FANP) to create a suitable platform for the realization of this interaction between managers and engineers and to identify exactly which expert system is ideal for the main purpose. Indeed, this chapter introduces its readers to the application of strategic management tools such as the BSC accompanied by FANP in the elements of supply chain management where data analysis of lean and agile networks in supply chain management can create a competitive advantage in the organization.