Search results

Are franking credit balances priced? This paper aims to investigate the valuation of franking credit balances via a determinant analysis and value relevance analysis.

The determinant analysis examines the factors that contribute to the increasing cumulative level of franking credit balances. Value relevance studies explore whether franking credit balances are priced in the market.

The results provide strong evidence of a size effect that the level of franking credit balances increases with firm size and weak evidence of an international focus effect that the level of franking credit balances increases with international ownership. They also find an individual dividend clientele effect that the level of franking credit balances decreases with individual ownership. They find significant evidence that franking credit balances are priced in the market. One dollar of franking credit is worth 1.4 dollars in firm value. That franking balances are capitalized at more than their face value suggests that franking credits signal firms' future dividend policy. They also find that the market valuation of franking balances increases with firm size but decreases with international focus.

This study provides direct evidence that franking credit balances are capitalized into equity prices. In the determinant analysis, this paper improves Heaney's (2009) model by using the percentage of international ownership as the proxy of international focus, thus addressing the limitation of his measure. In the value relevance tests, the study uses a modified model that includes log-transformation to reduce the skewness of variables based on Tanza's (2014) value relevance model. Moreover, the study suggests that the market valuation of franking credit balances increases with firm size, which contradicts Heaney's (2009) findings.

The purpose of this study is to adapt the incompressible smoothed particle hydrodynamics (ISPH) method with artificial intelligence to manage the physical problem of double diffusion inside a porous L-shaped cavity including two fins.

The ISPH method solves the nondimensional governing equations of a physical model. The ISPH simulations are attained at different Frank–Kamenetskii number, Darcy number, coupled Soret/Dufour numbers, coupled Cattaneo–Christov heat/mass fluxes, thermal radiation parameter and nanoparticle parameter. An artificial neural network (ANN) is developed using a total of 243 data sets. The data set is optimized as 171 of the data sets were used for training the model, 36 for validation and 36 for the testing phase. The network model was trained using the Levenberg–Marquardt training algorithm.

The resulting simulations show how thermal radiation declines the temperature distribution and changes the contour of a heat capacity ratio. The temperature distribution is improved, and the velocity field is decreased by 36.77% when the coupled heat Cattaneo–Christov heat/mass fluxes are increased from 0 to 0.8. The temperature distribution is supported, and the concentration distribution is declined by an increase in Soret–Dufour numbers. A rise in Soret–Dufour numbers corresponds to a decreasing velocity field. The Frank–Kamenetskii number is useful for enhancing the velocity field and temperature distribution. A reduction in Darcy number causes a high porous struggle, which reduces nanofluid velocity and improves temperature and concentration distribution. An increase in nanoparticle concentration causes a high fluid suspension viscosity, which reduces the suspension’s velocity. With the help of the ANN, the obtained model accurately predicts the values of the Nusselt and Sherwood numbers.

A novel integration between the ISPH method and the ANN is adapted to handle the heat and mass transfer within a new L-shaped geometry with fins in the presence of several physical effects.

The purpose of this study is to investigate the prevalence of earnings management in the Australian not-for-profit (NFP) disability service providers sector, as well as to understand the motivations for and implications of such practices. This research is important for stakeholders, such as members and funders, as well as the broader Australian community, considering the significant financial resources allocated to these organizations from the public purse.

The authors employ a longitudinal dataset containing financial data from 154 Australian NFP disability service providers, collected over a two-year period (2015–2016). Through the analysis of detailed balance sheets and income statements, the authors seek to uncover evidence of earnings management practices in this sector. The study’s results provide valuable insights into the behaviour of the charitable human services sector.

The findings reveal that Australian NFP disability service providers engage in earnings management practices, primarily aimed at reducing reported profits to meet the normative financial expectations of stakeholders, such as public sector funders and philanthropists. The executives of these organizations strive to report profits close to zero, being cautious not to report a loss, which might raise concerns about their sustainability.

The authors contribute to the existing literature on earnings management in the NFP sector by focussing on Australian disability service providers, an area that has been under-researched due to a lack of suitable data. The results offer insights into the incentives and implications of earnings management practices in this sector and highlight the need for a revaluation of accounting standards, reporting requirements and audit arrangements applicable to the NFP sector.

This study empirically investigates the relationship between goodwill assets and capital structure (i.e. debt ratio) of firms and the moderating effect of financial market development on the relationship between goodwill assets and capital structure.

This research applied a quantitative method. The article collects large samples of listed firms from 23 developing and nine developed countries and applied the panel data techniques. This research used firm-level data from the DataStream database for both developed and developing countries. The study uses 4,912 firm-level data from 23 developing countries and 4,303 firm-level data from nine developed countries.

The findings reveal a significant positive relationship between goodwill assets and capital structure in developing countries, but goodwill assets have a significant negative relationship with capital structure in developed countries. Moreover, financial market development positively moderates the relationship between goodwill assets and the capital structure of firms in developing countries. The results inform firm managers that goodwill assets serve as additional collateral to secure debt financing. Moreover, policymakers should formulate a debt market policy that recognizes goodwill assets as additional collateral for the purpose of obtaining debt capital.

The study has several implications. First, goodwill assets are identified as a factor of capital structure in this study. Fixed assets have been identified as one of the drivers of capital structure in previous research, although goodwill assets are seldom included. Second, this article shows that along with demand-side determinants, supply-side determinants also play an important role in terms of the firms' choice about the capital structure. Therefore, firms should take both the demand-side and supply-side factors into consideration when sourcing for external financing (i.e. debt capital).

The study considered goodwill as a component of capital structure. The study analysis includes a large sample of enterprises, including 4,912 big firms from 23 developing countries and 4,303 large firms from nine industrialized or developed countries, which adds to the current capital structure information. Furthermore, a large sample size increases the results' robustness and generalizability.

Manufacturing industries are facing dynamic challenges in today’s highly competitive world. In the recent past, integrating Industry 4.0 with the lean six sigma improvement methodologies has emerged as a popular approach for organizational excellence. The research aims to explore and analyze critical success factors of lean six sigma integrated Industry 4.0 (LSSI).

This research study explores and analyzes the critical success factors (CSFs) of LSSI. A three-phase study framework is employed. At first, the CSFs are identified through an extensive literature review and validated through experts’ feedback. Then, in the second phase, the initial list of CSFs is finalized using the fuzzy DELPHI technique. In the third phase, the cause-effect relationship among CFSs is established using the fuzzy DEMATEL technique.

A dyadic relationship among cause-and-effect category CSFs is established. Under the cause category, top management commitment toward integrating LSSI, systematic methodology for LSSI and organizational culture for adopting changes while adopting LSSI are found to be topmost CSFs. Also, under the effect category, organizational readiness toward LSSI and adaptability and agility are found to be the uppermost CSFs.

The study offers a framework to understand the significant CSFs for LSSI implementation. Insights from the study will help industry managers and practitioners to implement LSSI and achieve organizational excellence.

To the best of the authors’ knowledge, CSFs of LSSI are not much explored in the past by researchers. Findings will be of great value for professionals in developing long-term operations strategies.

Alain Bouchard was born in 1949. He bought his first convenience store in 1978, when he was almost 30 years old. By then, he already had nearly 10 years of experience in the sector. He had already been involved in the start-up of more than 200 convenience stores. He understood that if he was to transform his newly acquired store into a chain and build something big, he needed to set up a team of people with complementary skills to help him make acquisitions.

In 2023, there are roughly 15,000 convenience stores operating under the Circle K/Ingo/Couche-Tard banners, employing 130,000 people in more than 30 countries. Annual sales are more than US$60 billion. Alain Bouchard officially retired from his position as President and CEO in 2014 and became Founder and Executive Chairman of the Board. He continues to be a major shareholder. He is still actively involved in strategic orientations and in identifying potential acquisitions. He has become a ‘Chief Culture Officer’ involved in executive leadership mentoring. He has never stopped communicating the importance of innovative, creative and intrapreneurial behaviour at all levels of the enterprise.

This case study presents Alain Bouchard, the man and the entrepreneur. It shows how he learned and mastered the craft of starting, acquiring, managing and developing convenience stores. It looks at how he encouraged the people around him to act as facilitators and intrapreneurs. It describes his values, how he works and learned to live with risk.

The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya.

The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs.

The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable.

A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent.

While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.

This paper aims to investigate the non-homogeneity in the speed of adjustment (SoA) of the capital structure of manufacturing companies. It also attempts to study the key determinants that accelerate the speed of adjustment towards the target leverage level.

Using the dynamic panel fraction (DPF) estimator on the partial adjustment model, the study captures the heterogeneous SoA of 2,866 firms across eight prominent sectors of the Indian manufacturing industry from 2009 to 2020. To ensure robustness, the empirical inferences of DPF are cross-verified with the estimates of panel-corrected standard errors (PCSE).

The authors find a combination of the capital structure's slow, moderate and rapid adjustment speed along with the relevance of trade-off theory. Interestingly, the lowest and fastest SoA is recorded by the dwindling textile sector and expanding food and agro sector, respectively. Profitability, firm size, asset tangibility and non-debt tax shields are the key firm-specific parameters that impact the SoA towards the target.

Availing the rarely employed estimator ‘DPF’ and the objective of documenting diverse and non-uniform adjustment speeds across the Indian manufacturing sectors marks a novel addition to capital structure literature.