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Prior studies suggest that, in an industry in which several public firms operate (i.e. greater public firm presence), uncertainty about business operations within the industry is reduced due to greater analyst coverage and quality of information disclosure. In this study, the authors examine how UK private firms respond to investment opportunities in fixed intangible assets (FIAs) in an environment characterised by greater public firm presence (PFP).

Using data from 61,278 (1,358) private (public) UK firms operating in ten sectors spanning from 2006 to 2016, the authors conduct this analysis by using panel econometric techniques.

The authors observe that private firms are more responsive to their FIA investment opportunities when they operate in industries with more PFP. Also, the authors find that firms in industries with better information quality use more debt and have longer debt maturity security but less internal cash flow. Overall, the findings indicate that PFP generates positive externalities for private firms by lessening industry uncertainty and enhancing more efficient FIA investment. The results are robust to endogeneity concerns.

A key limitation of the study is that it focuses on a single country (the UK) and therefore there is a likelihood that the results found are specific to this setting but not others, particularly developing and emerging economies. Thus, future studies could explore these ideas from the viewpoint of multiple countries.

Overall, the study demonstrates the importance of information disclosure in driving investment decisions of firms.

While this paper builds on the information disclosure and corporate investment literature, it is one of the first attempts, to the best of the authors’ knowledge, to explore how private UK firms respond to investment in FIAs in an environment characterised by greater PFP.

The purpose of this study is to analyze the intersection of research on impact investing and its closely related financial vehicles.

The paper explores 196 articles collected from Scopus and Web of Science using bibliometric and content analysis methodologies.

Despite a growing academic interest in impact investing, scholars generally investigate impact investing as a social phenomenon, using the specific financial mechanism of social impact bonds. This perspective potentially deflates the complex nature of impact investing, which actually combines both social and financial targets and uses a plurality of financial vehicles to reach its goals.

The emerging themes identified will provide both academics and practitioners additional tools to further the debate on impact investing and the understanding of its potential and limits according to the different financial forms it takes. This review should pave the way for a discussion about the boundaries of the social impact sector itself.

Despite the strong international commitment toward impact investing, tensions still exist. A comprehensive overview on the relevant aspects not yet thoroughly investigated will foster the growth of impact investments.

To the best of the authors’ knowledge, this is the first holistic overview of impact investing, that jointly examines both literature on impact investing and literature on the correlated financial products used in the industry. The result is a comprehensive report of what is known about impact investing in its different financial forms, opening up new pathways for future studies.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

This case study is related to start-up post-listing investment analysis. Through this case study, students will be able to perform the business analysis guided by the Venture Evaluation Metric tool, perform financial analysis using the discounted cash flow methods and perform investment analysis recommendation with justifications from the business and financial analysis performed above.

This case study sets out the study of a scalable start-up, Zomato, which is a successfully listed start-up firm in India. Despite the start-up development success in the pre-listing, the firm has exhibited a continuous unprofitable finance performance in the post-listing and has further experienced a volatile share price performance, both of which have puzzled existing and potential investors. In addition, some analysts are in the opinions that the firm share price valuation have been inflated with overvaluation since in the initial public offering stage and remain traded with overvaluation in the market. Notably, considering the negative indicators mentioned above, investors are concerned about long-term sustainability of the firm business and financial performance. In the context of post-listing investment, the following questions are material to investors: What is the realistic growth trajectory for Zomato in the medium term? What is Zomato’s share fair value in the medium term? Can one see opportunities or risks ahead of investing in Zomato’s shares? What will be the investment strategy for new investors?

This case study is suited to bachelor’s and master’s level in business schools studying entrepreneurial finance analysis.

Teaching notes are available for educators only.

CSS 1: Accounting and finance.

This instrumental case study (Stake, 1995) explores the benefits experienced by mentor teachers who mentored resident teachers in a year-long residency program.

The study was grounded by the Benefits of Being a Mentor conceptual framework, as defined by Ragins and Scandura (1999). The participating mentor teachers engaged in semi-structured interviews and a focus group. The data were analyzed through inductive and deductive data analysis techniques.

Using inductive and deductive data analysis techniques, three themes emerged on the benefits of being a mentor teacher: (1) extra support in the classroom, (2) professional learning and growth opportunities, and (3) investing in the future of education. The teachers’ perceived benefits were related to the connectedness of their personal and professional growth, the growth of the resident teachers and their students’ learning.

Mentor teachers play a vital role in teacher residency programs, as they are the primary influence on their resident teachers’ pedagogical praxis. In a residency program, mentor teachers support resident teachers’ sustained teaching experience by hosting them for one full academic year in their classrooms; therefore, exploring the benefits they receive from serving in this role is essential.

An interview with Zeynep Ton, a professor of practice in the operations management group at MIT Sloan School of Management, about er latest book, The Case for Good Jobs: How Great Companies Bring Dignity, Pay & Meaning to Everyone’s Work.

She believes that leaders can either view their employees as a cost to be minimized, invest little in them and operate with high turnover, or they can see them as drivers of profitability and growth—investing heavily in them, designing their work for high productivity and contribution and therefore operating with low turnover.-- “the good jobs strategy.”

The secret sauce of good jobs strategy is four operational choices—focus and simplify, standardize and empower, cross-train and operate with slack—that improve productivity and contribution and make that higher investment possible.

The competitive costs of low people investment are even higher than the poor operational execution costs.

By making the work better and increasing pay, companies can better attract and keep their talent and enforce high standards, which improve execution and service, uplifting revenue. Few have examined this important topic more closely than Zeynep Ton, a professor of practice in the operations management group at MIT Sloan School of Management, best-selling author of The Good Jobs Strategy: How the Smartest Companies Invest in Employees to Lower Costs and Boost Profits.

The financial sector’s growing interest in leveraging artificial intelligence (AI) for forecasting has been noted in recent years. In this chapter, we delve into the application of AI in financial forecasting within Indonesia’s stock market. Our primary focus is to assess how AI’s prediction potential can impact investors and financial regulators in this context. Our review spans existing literature on AI and financial forecasting, recent developments in the Indonesian stock market, and ethical and regulatory concerns that surround AI in finance. Our analysis indicates that AI can enhance forecast accuracy in Indonesia’s stock exchange; however, we must also consider limitations and challenges.

Countries worldwide aim to improve their comparative advantages by efficiently using scarce resources for economic growth and development. While many studies have been conducted to measure intellectual capital at the firm's level, measuring it at the national level has been under-examined. In addition, while the important role of national intellectual capital in economic growth has been theoretically recognized in literature, this important link has largely been ignored in empirical analyses.

This study uses the newly developed index of national intellectual capital from Vo and Tran's (2022) study to examine its effects on national economic growth in the long run. The dynamic common correlated effects technique and the pooled mean group estimation are used on the sample of 23 economies in the Asia–Pacific region from 2000 to 2020.

Findings from this study confirm the positive and significant contribution of the national intellectual capital to economic growth in the region. The authors also find that, as a feedback effect, economic growth will also enhance and improve the accumulation of national intellectual capital.

The findings of this paper provide valuable evidence and implications for policymakers in managing and improving national intellectual capital in the Asia–Pacific region.

To the best of the authors’ knowledge, this is the first empirical study to examine the impact of national intellectual capital on economic growth in the long run in the Asia–Pacific economies.

Many studies have analysed the impact of various variables on the ability of companies to raise capital. While most of these studies are sector-agnostic, literature on the effects of macroeconomic variables on sectors that established over the last 20 years like property technology and financial technology, is scarce. This study aims to identify macroeconomic factors that influence the ability of both sectors and is extended by real estate variables.

The impact of macroeconomic and real estate related factors is analysed using multiple linear regression and quantile regression. The sample covers 338 observations for PropTech and 595 for FinTech across 18 European countries and 5 deal types between 2000–2001 with each observation representing the capital invested per year for each deal type and country.

Besides confirming a significant impact of macroeconomic variables on the amount of capital invested, this study finds that additionally the real estate transaction volume positively impacts PropTech while the real estate yield-bond-gap negatively impacts FinTech.

For PropTech and FinTech companies and their investors it is critical to understand the dynamic with mac-ro variables and also the real estate industry. The direct connection identified in this paper is critical for a holistic understanding of the effects of measurable real estate variables on capital investments into both sectors.

The analysis fills the gap in the literature between variables affecting investment into firms and effects of the real estate industry on the investment activity into PropTech and FinTech.

This study provides empirical evidence on the impact of the Panama Canal expansion (PCE) on the economies of Latin American and Caribbean (LAC) countries, particularly in light of the emergence of larger container ships such as neo-Panamax and post-Panamax vessels.

This study uses the Bayesian structural time Series (BSTS) model to evaluate the economic effects of the PCE on 21 countries within the LAC region. It utilized the World Bank's gross domestic product (GDP) figures between 2000 and 2019 as the primary variable, alongside the human development index (HDI) (X1), container throughput (TEU) (X2) and unemployment rates (UNEMPL) (X3) covariates. This allowed a precise and robust approach to analyzing time series data while accounting for uncertainties and allowing the inclusion of various components and external factors.

The findings revealed that the PCE has a positive and statistically significant impact on most countries within the Caribbean Transshipment Triangle, ranging from 9.2% in Belize to 46% in Cuba. This suggests that the causal effect of the PCE on regional economies was not confined to any specific type of economy or geographical location within the LAC region. Where the growth rates were statistically insignificant, primarily in some Latin American countries, it coincided with countries that are primarily driven by exports and service industries, where bulk and oil tanker vessels are likely to be the main carriers for exports rather than container vessels.

The practical implications of this research are crucial for various stakeholders in the maritime industry and economic planning. The factors influencing economic growth resulting from investing in maritime activities are vital for decision-makers to create policies that lead to positive outcomes and sustainable development in regions and countries with flourishing maritime industries. The methodology and findings have significant implications for governments, managers, professionals, policy-makers and investors.