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This study investigates the efficiency of value relevance and faithful representation when stock market price derivates from its firm value to the investigated IT companies listed in FTSE Taiwan 50. The empirical investigation reveals one financial indicators: Return on equity (ROE) has explanatory ability among seven financial indicators, earnings per share (EPS), book value (BV), dividend yield (Div.), price–earnings ratio (P/E), ROE, return on assets (ROA), and return on operating asset (ROOA) to both sampled companies, United Microelectronics Corporation, UMC, (2303) and Taiwan Semiconductor Manufacturing Company Limited, TSMC, (2330). Furthermore, the empirical results indicate that the higher order moments, skewness and kurtosis, of price deviation do not provide a reliable prediction or explanatory power for stock price trends.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

By examining the impact of the day of the week during the COVID-19 pandemic and the subsequent economic recession, it is possible to provide insights into market behaviour during volatile times that can be furnished to investors and policymakers for informed decisions.

This study investigates the day-of-the-week effect on the Colombo Stock Exchange (CSE), with particular emphasis on the variations in this effect during the COVID-19 pandemic and the subsequent economic crisis.

The study applies the Exponential Generalised Autoregressive Conditional Heteroskedasticity (EGARCH) model, allowing for the evaluation of asymmetric responses to positive and negative shocks. The data span from January 2006 to December 2022 and are segmented into different periods: the entire sample, war and post-war periods, the COVID-19 pandemic and the economic crisis period, each reflecting distinct market conditions.

The study uncovers a significant day-of-the-week effect on the CSE. Mondays and Tuesdays typically show a negative effect, while Thursdays and Fridays display a positive impact. However, this pattern shifts notably during the COVID-19 pandemic, with all weekdays exhibiting significant positive impact, and varies further across different waves of the pandemic. The economic crisis period also shows unique weekday effects, particularly before and after an important political event.

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.

Purpose: The purpose of this study is to identify the elements that can enhance financial inclusion (FI) in a nation, which in turn promotes economic development and growth.

Need for the Study: FI is crucial in providing people with the skills and resources to manage their money effectively and make informed financial decisions. Accessible, reliable and secure financial services play a significant role in achieving sustainable development goals (SDGs) and fostering economic progress.

Methodology: Data from 571 respondents were collected for analysis. The study utilises Statistical Package for Social Sciences SPSS and Analysis of Moment Structures AMOS software to analyse data and achieve the study’s objectives. The researchers employ these tools to obtain substantial results.

Findings: The findings indicate that FI contributes to economic growth (84%) and helps in accomplishing SDGs. Access, usage, affordability, technology, availability and technology adoption all play a vital role in increasing FI in the nation.

Practical Implications: The study’s outcomes have practical implications for policymakers and stakeholders, emphasising the importance of promoting FI through various measures such as enhancing access, affordability and technological advancements in financial services.

Customer experience is increasingly recognized as a source of competitive advantage. Customer experience refers to customers' responses and reactions to cues within touchpoints along customer journeys. Nowadays, customers often interact with online touchpoints – such as social media, websites, or e-commerce – in their customer journeys. Given that customer experience is multidimensional, this chapter addresses the following question: How can sensorial experiences be triggered in online touchpoints? Based on a review of the literature on customer experience and sensory marketing, four challenges in triggering sensorial experiences in online touchpoints are identified: (1) limited sensorial cues, (2) lack of thematic congruence between online and offline touchpoints, (3) sensory overload, and (4) lesser control over sensorial cues. Then, two routes through which organizations can trigger sensorial experiences in online touchpoints are proposed: (1) directly influencing sensations through sensory-enabling technologies, and (2) indirectly influencing sensorial perceptions through the use of sensory and nonsensory cues. The chapter closes with a presentation of a model that describes the process of triggering sensorial experiences in online touchpoints as well as a checklist of relevant questions for practitioners who wish to do so.