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This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

The authors’ goal is to provide an overview and historical context for the various alternatives to the efficient market hypothesis (EMH) that have emerged over time. The authors found eight current alternatives that have emerged to address the EMH's flaws. Each of the proposed alternatives improves some of the assumptions made by the EMH, such as investor homogeneity, the immediate incorporation of information into asset values and the inadequacy of rationality to explain asset prices.

To come up with the list of studies relevant to this review article, the authors used three databases, namely Scopus, Web of Science and Google Scholar. The first two were mostly used to get peer-reviewed articles while Google Scholar was used to extract articles that are still work in progress. The following words were used as the search queries; “efficient market hypothesis” and “alternatives to the efficient market hypothesis”.

The alternatives to the EMH presented in this article demonstrate that market efficiency is a dynamic concept that can be best understood with a multidisciplinary approach. To better comprehend how financial markets work, it is crucial to draw on concepts, theories and ideas from a variety of disciplines, including physics, economics, anthropology, sociology and others.

The authors comprehensively summarise the current state of the behavioural finance literature on alternatives to the EMH.

This study aims to analyse the non-linear losses of a porous media (stack) composed by parallel plates and inserted in a resonator tube in oscillatory flows by proposing numerical correlations between pressure gradient and velocity.

The numerical correlations origin from computational fluid dynamics simulations, conducted at the microscopic scale, in which three fluid channels representing the porous media are taken into account. More specifically, for a specific frequency and stack porosity, the oscillating pressure input is varied, and the velocity and the pressure-drop are post-processed in the frequency domain (Fast Fourier Transform analysis).

It emerges that the viscous component of pressure drop follows a quadratic trend with respect to velocity inside the stack, while the inertial component is linear also at high-velocity regimes. Furthermore, the non-linear coefficient b of the correlation ax + bx² (related to the Forchheimer coefficient) is discovered to be dependent on frequency. The largest value of the b is found at low frequencies as the fluid particle displacement is comparable to the stack length. Furthermore, the lower the porosity the higher the Forchheimer term because the velocity gradients at the stack geometrical discontinuities are more pronounced.

The main novelty of this work is that, for the first time, non-linear losses of a parallel plate stack are investigated from a macroscopic point of view and summarised into a non-linear correlation, similar to the steady-state and well-known Darcy–Forchheimer law. The main difference is that it considers the frequency dependence of both Darcy and Forchheimer terms. The results can be used to enhance the analysis and design of thermoacoustic devices, which use the kind of stacks studied in the present work.

The study aims to evaluate the influence of capital structure decisions and asset structure on firms' performance for East African listed nonfinancial firms.

The research is descriptive and employs secondary data from the East African capital markets' websites. The generalized method of moments approach is used to estimate the relationship due to its ability to account for endogeneity problems.

The result shows that capital structure decisions and asset structure strongly influence the firms' performance. When long-term debts, short-term debts and tangible fixed assets increase, the return on total assets increases. An increase in the total debt ratio raises the return on equity (ROE). However, the increase in long-term debt lowers the ROE.

The results will help investors and potential investors decide on a financing policy that maximizes performance. Likewise, governments and other policymakers review the capital markets' frameworks to attract institutional and individual investors to the markets for financial availability and to increase profitability.

The research provides evidence on the influence of capital structure decisions and asset structure on firms' performance. Furthermore, its results contribute to firms' financing policy formulation and the corporate finance literature.

This research provides some evidence by the vine copula approach, suggesting the significant and symmetric causal relation between subsections of Baltic Exchange and hence concluding that investing in different indexes, which is currently a risk diversification system, is not a correct risk reduction strategy.

The daily observations of Baltic Capesize Index (BCI), Baltic Handysize Index (BHSI), Baltic Dirty Tanker Index (BDTI) and Baltic LNG Tanker Index (BLNG) over an eight-year period have been used. After collecting data, calculating the return and estimating the marginal distribution of return rates for each of the indexes applying asymmetric power generalized autoregressive conditional heteroskedasticity and autoregressive moving average (APGARCH-ARMA), and with the assumption of skew student's t-distribution, the dependence of Baltic indexes was modeled based on Vine-R structures.

A positive and symmetrical correlation was observed between the study groups. High and low tail dependence is observed between all four indexes. In other words, the sector business groups associated with each of these indexes react similarly to the extreme events of other groups. The BHSI has a pivotal role in examining the dependency structure of Baltic Exchange indexes. That is, in addition to the direct dependence of Baltic groups, the dependence of each group on the BHSI can transmit accidents and shocks to other groups.

Since the Baltic Exchange indexes are tradable, these findings have implications for portfolio design and hedging strategies for investors in shipping markets.

Vine copula structures proves the causal relationship between different Baltic Exchange indexes, which are derived from different types of markets.

The paper investigates if the process that led to the birth of the Euro Area had a significant impact in homogenizing the capital structure decisions of European firms since the first introduction of the common currency.

A large sample of firms was constructed, and a Tobit-censored regression model was utilized to investigate the determinants of firms' observed capital structures. The Black–Scholes–Merton model was used to infer market values of assets, as well as the volatility of those values, from the observed market values of equity and the corresponding volatility. The existing differences in national tax rules were considered for estimating firm-specific marginal tax rates.

It was found that, despite the currency union and the institutional harmonization process, certain factors still play a different role. In particular, the impact of profitability is consistent with the pecking order view in some countries, and with the trade-off theory in others. Assets risk, measured as the annualized volatility of the market enterprise value, is the best predictor of observed leverage ratios. The sector of activity is significant in determining leverage decisions even when assets' risk is taken into account. Despite the monetary union and the increased financial and institutional integration in the Euro Area, the country of origin still plays a significant role in capital structure decisions, suggesting that other country-level factors may affect firms' financing behaviour.

The paper indicates that, despite the long harmonization process of institutions, regulations and public budget required to join the Euro, firms' financing decisions are still affected by country-specific factors once the common currency is introduced. Therefore, new entrant countries in the Euro area should not expect their companies to immediately conform with those located in other countries within the common currency area.

This article investigated the impact of the currency change from national currencies to the Euro on the determinants of capital structure choices. It was shown that, despite the long harmonization process that led to the birth of the Euro Area, national factors still affect firms' financing decisions. This provides guidance for policymakers in countries that are planning to join the Euro about the impact this will have on firms' financing decisions in the entrant country.

India started economic reforms at a rapid pace to catch up the world economy by following the services-led-growth model during the post-liberalisation period. Over the years, the growing unemployment rate posits a re-look into the dynamics of growth model for wider work force participation. In this backdrop, the paper aims to examine the dynamics of structural changes in employment pattern in view of economic growth led by services-led growth model in India.

The study employs a non-linear autoregressive model (NARDL) to examine the effect of the growth rates in three broad economic sectors namely agriculture and allied, services and industry on work force participation representing the employment opportunities in India.

The results highlight that the rapid expansion of the service sector has not occurred with enough employment opportunities by the same rate. By contrast, the growth in the industrial sector significantly creates employment opportunities in the short and long run. These results support the industry led growth model over the services for sustainable and inclusive economic growth in the country.

The study relies on combined labour force participation rates rather than gender-specific rates. Further, the regulatory, working conditions and economic incentives may affect the gender-specific engagement of the labour force in three broad sectors.

The results offer important insight into changing patterns in employment with policy lessons. A wider workforce force participation calls for expansion of manufacturing activities through pro-industry programmes.

The study makes pioneer efforts to examine the dynamics of labour force participation with respect to the growth of three broad economic sectors of the Indian economy. The results provide new insights with policy implications for the changing employment pattern and policy response.

This study examines the co-integration relationships between Association of Southeast Nations (ASEAN) stock indices as a way to assess the feasibility of policy initiatives to strengthen market integration in ASEAN and identify implications for portfolio investors.

The authors employ threshold co-integration tests and a non-linear autoregressive distributed lag (NARDL) model to study the asymmetric dynamics of ASEAN equity markets. The study’s data cover the 2009–2022 period for seven member states: Cambodia, Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam.

The authors find evidence supporting co-integration relationships; adjustment toward equilibrium is asymmetric in the short run and symmetric in the long run for these countries. While co-movement in ASEAN equity markets seems encouraging for initiatives seeking to foster financial integration in regional economies, the benefits for international portfolio diversification appear to be neutralized.

The issue of stock market integration is important among policymakers, investors and academics. This study examines the level of stock market integration in ASEAN during the 2009–2022 period. For this purpose, advanced co-integration techniques are applied to different frequencies of data (daily, weekly and monthly) for comparison and completeness. The empirical analysis of this study is conducted using the Enders and Siklos (2001) co-integration and threshold adjustment procedure. This advanced co-integration technique is superior compared to other co-integration techniques by permitting asymmetry in the adjustment toward equilibrium.

This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.

Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.

The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.

The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.

This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.

The authors explore the relationship between the exchange rate, bond yield and the stock market as well as the effect of capital market dynamics on the exchange rate before and during the COVID-19 pandemic.

The authors employ a non-linear autoregressive distributed lag (NARDL) methodology using daily data of the Indonesian economy over the period 2012–2021.

Whilst, over the full sample period, the authors find no cointegration between the exchange rate, the 10-year bond yield and stock market, for the COVID-19 period, evidence of cointegration is present. Furthermore, the results suggest that asymmetric effects are evident both in the short as well as the long run.

To the best of the authors’ knowledge, this is the first time that the relationship between the exchange rate, bond yield and the stock market as well as the effect of capital market dynamics on the exchange rate before and during the COVID-19 pandemic has been explored in the case of the Indonesian economy.