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This study aims to explore the differences in the tourists’ perceived destination image on travel e-commerce platforms (e.g. Ctrip and Fliggy) and social media platforms (e.g. Xiaohongshu and Weibo).

This study employs firm-level data to evaluate how the knowledge economy impacts the financing constraints of businesses across 106 low- and middle-income nations, focusing on the influence of technological transformation on corporate financing choices.

The research centers on privately held, unlisted firms and examines the distinct effects of knowledge at both the within-country and between-country levels using a panel dataset. Rigorous sensitivity and endogeneity analyses are conducted to ensure the reliability of the findings.

The findings indicate that greater levels of the knowledge economy correlate with reduced financing constraints for firms. However, this effect varies depending on the location within a country and across different geographical regions. Firms situated in larger urban centers and more innovative regions reap the most significant benefits from the knowledge economy when seeking external funding. Conversely, firms in smaller cities, rural areas and regions characterized by structural and institutional inefficiencies in knowledge generation experience fewer advantages.

The impact of knowledge exhibits variability not only within and among countries but also between poor and affluent developing nations, as well as between larger and smaller countries. The knowledge effect on firms' access to external finance is influenced by factors such as financial openness and development, educational quality, technological absorption capabilities and agglomeration conditions within each country.

The airport's international recognition and attractive potential by awards' private and public organisations or governments have awakened a great level of interest from airport and airline operators, researchers and academics in the aviation industry. Some of the most important prestigious aviation and tourism accolades worldwide, like the World Airport Awards (Skytrax), Airports Council International (ACI) World's annual Airports Service Quality ‘ASQ Awards’ (Airport Council International) and the World Travel Awards (World Travel & Tourism Council), recognise and reward the excellence of services and products provided by airports and airlines. Consequently, the winning airports have global recognition depending on the award's category and empower the airport's brand image against its main competitors. These awards have become true marketing windows, where all airports want to win to promote their brand image and distinguish the airport's position as a world leader in the aviation industry. This topic and the dissemination of information at the airport's website and other digital channels will be tackled in Chapter 8.

This paper aims to assess the efficiency of emulsified essential oils in glycerol as eco-friendly antimicrobial and plasticized agents added to the biopolymer of gelatin for lining historical oil paintings on canvases.

Cedar oil, cinnamon oil and their mixtures were emulsified in glycerol and incorporated into gelatin adhesive as green biocides and plasticizers. Physical, biological, chemical and mechanical tests were conducted on experimental mock-ups to assess the gelatin-based adhesive formulations for the reinforcement of canvas supports. Scanning electron microscope, colorimetric measurements, antimicrobial activity test, attenuated total reflection-Fourier transform infrared spectroscopy, tensile strength and elongation tests were carried out on the mock-ups before and after the artificial aging.

The formulations of gelatin-based adhesive with cinnamon and cinnamon-cedar mixture emulsified in glycerol proved their efficiency on the antimicrobial activity test, chemically delaying the decomposition of gelatin and accordingly providing compatible mechanical properties. Gelatin-based adhesive with emulsified cinnamon oil showed a slight yellowing that was quite improved with the mixture of the cinnamon-cedar-based adhesive formulation.

This study promotes a green approach to lining historical oil paintings by developing green formulations from bio-based origins that minimize the shrinkage and microbial infection of gelatin for lining paintings.

This study aims to examine the impact of manager sentiment on the firm performance (FP) of Indian-listed nonfinancial firms. Further, it endeavors to investigate the moderating role of economic policy uncertainty (EPU) and environment, social and governance (ESG) transparency in this relationship.

A noble manager sentiment is introduced using FinBERT, a bidirectional encoder representation from a transformers (BERT)-type large language model. Using this deep learning-based natural language processing approach implemented through a Python-generated algorithm, this study constructs a manager sentiment for each firm and year based on the management discussions and analysis (MD&A) report. This research uses the system GMM to examine how manager sentiment affects FP.

The empirical results suggest that managers’ optimistic outlook in MD&A corporate disclosure sections tends to present higher performance. This positive association remains consistent after several robustness checks – using propensity score matching and instrumental variable approach to address further endogeneity, using alternative proxies of manager sentiment and FP and conducting subsample analysis based on financial constraints. Furthermore, the authors observe that the relationship is more pronounced for ESG-disclosed firms and during the low EPU.

The results demonstrate that the manager sentiment strongly predicts FP. Thus, this study may provide valuable insight for academics, practitioners, investors, corporates and policymakers.

To the best of the authors’ knowledge, this is the first study to predict FP by using FinBERT-based managerial sentiment, particularly in an emerging market context.

This paper aims to use three proxy variables – initial public offerings, trading volume and business confidence index (BCI) to construct an investor sentiment index both for the USA and China, taking into account the challenging periods of the COVID-19 pandemic and the Russo-Ukrainian conflict.

Based on monthly data for a period from January 2009 to June 2022, this paper uses data of BCI, consumer confidence index (CCI), gross domestic product, trading volume and Fama and French (1993) factor data; linear regression of single and multifactor model; and EGARCH-M model for analyzing the effect of investor sentiment on stock market return and volatility, both in the USA and China.

The empirical results indicate the suitability of BCI over CCI as a measure of investor sentiment, both in the USA and China. The results indicate that investor sentiment has a significant positive effect on the excess returns in the stock market in both countries. Moreover, the effect of investor sentiment is higher in China than it is in the USA. Such an effect of investor sentiment is significant and fluctuates asymmetrically in the short run but loses its significance in the long run. Optimistic investor sentiment has a larger effect on the stock market volatility in the USA, while the pessimistic investor sentiment has a larger effect in the Chinese stock market.

This paper focuses on finding a more suitable proxy for investor sentiment from BCI or CCI. This paper also contributes by including both optimism and pessimism in explaining the stock return and volatility in both markets. The overall findings are important for understanding investor behavior in different market conditions.

Online portfolio selection sequentially allocates wealth among a set of assets and aims to maximize the investor’s cumulative return in the long run. Various existing algorithms in the finance and accounting area adopt an indirect approach to exploit one asset characteristic through the channel of assets’ expected return and thus cannot fully leverage the power of various asset characteristics found in the literature. This study aims to propose new algorithms to overcome this issue to enhance investment performance.

We propose a parameterized portfolio selection (PPS) framework, which directly incorporates multiple asset characteristics into portfolio weights. This framework can update parameters timely based on final performance without intermediate steps and produce efficient portfolios. We further append L1 regularization to constrain the number of active asset characteristics. Solving the PPS formulation numerically, we design two online portfolio selection (OLPS) algorithms via gradient descent and alternating direction method of multipliers.

Empirical results on five real market datasets show that the proposed algorithms outperform the state of the arts in cumulative returns, Sharpe ratios, winning ratios, etc. Besides, short-term characteristics are more important than long-term characteristics, and the highest return category is the most important characteristic to improve portfolio performance.

The proposed PPS algorithms are new end-to-end online learning approaches, which directly optimize portfolios by asset characteristics. Such approaches thus differ from existing studies, which first predict returns and then optimize portfolios. This paper provides a new algorithmic framework for investors’ OLPS.

Press reports have indicated that firms frequently underprice restricted stock and employee stock options. I test for underpricing of stock and options.

I examined a sample of 5,333 private firm stock and option issuances between 1985 and 2017. I tested for underpricing using two approaches: assuming investors have no special market-timing ability and assuming instead they have perfect market-timing ability.

I find evidence of widespread stock and option underpricing by private firms before they go public reflecting large discounts that exceed reasonable compensation for lack of marketability. Unreported underpricing is more frequent in the last pre-IPO private equity transactions that offer the last opportunity to give such discounts before the stock is publicly traded, but the discounts are greater in the earlier pre-IPO transactions where unreported discounts are presumably tougher for the SEC to detect. Underpricing is still detected even when the actual DLOMs are tested against a benchmark that assumes investors have perfect market-timing ability.

Firms frequently underprice restricted stock and employee stock options. Firms tend to underprice stock options more frequently than restricted stock, but restricted stock tends to be priced at deeper discounts when recipients are assumed not to have any special market-timing ability.

Private firms issue restricted stock and options as incentive compensation. Lowballing the valuation transfers wealth from outside stockholders to employees/insiders. Wealth transfers take place through the issuance of equity claims to employees/insiders before firms go public. I found that more than a quarter of the DLOMs exceed the theoretical maximum by, on average, between 16% (median) and 20% (mean). This finding raises two questions worthy of investigation. First, to what extent do the frequency and magnitude of DLOMs above the theoretical maximum depend on whether a board of directors obtains an independent appraisal of a stock’s fair market value? Second, if DLOMs above the theoretical maximum are observed even when the stock is independently appraised, how do appraisers justify such large DLOMs?

The wealth transfers that take place through the issuance of equity claims to employees/insiders before firms go public benefit employees/insiders at the expense of outside shareholders.

My paper is the first to furnish evidence of widespread stock and option underpricing by private firms before they go public; demonstrate that the unreported underpricing is more frequent in the last pre-IPO private equity transactions that offer the last opportunity to give such discounts before the stock is publicly traded and show that the discounts are greater in the earlier pre-IPO transactions where unreported discounts are presumably tougher for the SEC to detect.

Retirement is a preparation for transitioning from one role to another and transitioning to a new stage of life. The deepening aging of the population encourages the policymakers to start the Bridge Employment plan when the society faces the unprecedented challenges of decreasing labor supply, heavier burdens of retirement and slow economic growth. The purpose of this study, the decision model for Bridge Employment has been developed by systematically reviewing the research literature.

A bibliometric analysis was conducted to cover publications on Bridge Employment for Return to Work published from 1994 to 2023, including a total of 1,936 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analyzed.

After carefully examining the results of the selected studies, three categories of individual (micro level), organizational (medium level) and contextual (macro level) factors were identified as effective factors on bridge employment and model development. Each of the mentioned factors, along with the legal, financial, managerial, educational-administrative and consulting requirements, form the basis for the development of the model and decision framework for Paul’s employment.

This model can be a basis as a framework for bridge recruitment planning so that organizations can once again use their skills–knowledge–expertise in different jobs and even training younger people.

Taking advantage of an innovative measure of corporate culture obtained from advanced machine learning and textual analysis, we investigate how corporate culture is influenced by shareholder litigation rights, which are widely recognized as a crucial external governance mechanism. The innovative measure of corporate culture is based on a textual analysis of over 200,000 earnings call transcripts.

To mitigate endogeneity and thus demonstrate causality, we exploit a quasi-natural experiment based on the staggered passage of universal demand laws, which reduce shareholder litigation rights. The enactment of state-level legislation is likely exogenous to individual firms’ characteristics as it is beyond the control of any given firm. Following the literature, we employ a difference-in-difference analysis, supplemented by several robustness checks, i.e. propensity score matching and entropy balancing.

Our difference-in-difference estimates show that an exogenous reduction in shareholder litigation rights weakens corporate culture considerably. Specifically, corporate culture is 12.74–14.41% weaker after the implementation of universal demand laws. Our results corroborate the hypothesis that a decline in litigation risk exacerbates agency problems, discouraging self-interested managers from taking actions that enhance shareholder value in the long run, such as cultivating a strong corporate culture.

Our study is the first to explore how corporate culture is affected by shareholder litigation risk, which constitutes a vital external governance mechanism. Moreover, we utilize an innovative measure of corporate culture based on sophisticated textual analysis. Finally, we employ a quasi-natural experiment based on an exogenous shock, making it more likely that our conclusion reflects a causal influence rather than merely a correlation.