Search results

This research intends to investigate the impact of economic policy uncertainty (EPU) on the firm's leverage and its adjustment speed.

This study applies dynamic panel data modeling by using a partial adjustment model. The study is based on secondary data of the non-financial firms that are listed on the Pakistan stock exchange. For the analysis purpose, the study applies the generalized method of moments (GMM) estimation technique and uses a newly developed text-based measure of economic policy uncertainty.

The results show the negative impact of EPU on leverage decisions but a positive impact of EPU on leverage speed of adjustment for both, short-run and long-run economic policy shocks. Additional analysis reveals that the negative influence of long-run policy shocks on leverage decisions is moderated through profitability, and the negative influence of short-run policy shocks on leverage is moderated through firm size, tangibility and available growth prospects. However, the significant positive impact of EPU on the leverage speed of adjustment in both short and long-term policy shocks indicates that the speed of adjustment for these firms is not affected by policy shocks.

This research contributes to the existing literature on capital structure dynamics,by investigating the impact of EPU on firm financing decisions and estimating the adjustment speed of capital structure in a developing market context. The study also extends the existing literature by applying the concept of long-run and short-run economic policy uncertainty in the capital structure dynamic framework. Additionally, the new news-based measure of EPU is used. Moreover, it also looks into the COVID-19 effect on the relationship.

The study investigates the relationship between corporate social responsibility (CSR) and dividend policy in the context of Indian firms, as well as how regulatory interventions in the form of mandated CSR can moderate this relationship.

A sample of the largest 500 companies listed on the National Stock Exchange from 2008 to 2019 is used in the study. The authors employ the system generalized method of moments since this estimation technique yields accurate and consistent findings in a dynamic panel data setting.

The authors find that CSR is positively associated with dividend payments. Increased incomes and lower financial constraints are the likely factors causing this relationship. Additional analysis suggests that the positive relationship is stronger for mature firms and for firms with higher information asymmetry. Financial reporting quality works in tandem with CSR to boost dividends. Regulatory interventions in the form of mandated CSR weaken the relationship. Finally, the speed of adjustment of dividends is relatively faster for socially responsible firms.

The positive association between CSR and dividends suggests that the interest of shareholders and other non-financial stakeholders can be reconciled. Additionally, businesses should attempt to strategically implement their CSR plans in accordance with the requirements of Section 135 of the Companies Act, 2013 to avoid any unfavourable moderating effects of the legislation. The results also show that CSR disclosures complement rather than serve as a substitute for financial disclosures.

The study is the first to evaluate the relationship between CSR and dividends in the context of India, which is a pioneer in passing legislation that mandates CSR for firms surpassing a threshold. The authors also identify financial constraints as a channel through which CSR affects dividends.

This study examines the influence of economic policy uncertainty on financial flexibility before and during the coronavirus disease 2019 (COVID-19) pandemic. Few prior studies have examined this association specifically for debt and cash flexibility.

Using quarterly data from 2016 to 2022, 1014 observations were collected from the S&P Capital IQ database for listed tourism companies in India. The pre-pandemic period is defined as 2016 Q1 to 2020 Q1, whereas the pandemic period is from 2020 Q2 to 2022 Q3. The data are analysed using ordinary least squares, probit, logit and difference-in-difference (DID) estimation.

The evidence of this study suggests a negative association of economic policy uncertainty with debt flexibility during the COVID-19 pandemic. The findings also suggest that COVID-19 induced economic policy uncertainty results in high cash flexibility. This meets the expectations for the crisis period, as firms are likely to hold more cash and less debt capacity to manage their operations. The results are robust for various estimation techniques.

This study is limited to one emerging country and is specific to one non-financial sector. Future research could extend to more emerging countries and include other non-financial sector companies.

The findings of this research are useful for tourism sector managers as they can effectively manage their cash and debt flexibility during crisis periods. They will need to prioritise cash flexibility over debt flexibility to manage operations effectively. Policymakers need to provide clear and stable economic policies to help firms manage their debt levels during a crisis.

To the best of the author's knowledge, no existing studies have investigated the influence of economic policy uncertainty on the financial flexibility of tourism companies before and during the COVID-19 pandemic. Furthermore, this study establishes a novel set of critical determinants, such as economic policy uncertainty.

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.

The aim of this study is to empirically examine firm-specific factors that influence the financing decisions of companies listed on BSE-500 index. Firm-specific variables such as profitability, company size, growth potential, liquidity, non-debt tax shields, age and tangibility were evaluated in this study.

This empirical research is performed using longitudinal data of 366 companies listed on the BSE 500 index during 2006–2020. Pooled ordinary least square method is employed to classify primary determinants of capital structure.

The results show that profitability, liquidity and non-debt tax shield are negatively associated whereas, company size, growth potential, age and tangibility are positively associated with the capital structure. The authors’ observations are aligned with either the trade-off hypothesis or the principle of the pecking order.

This study helps to better understand how firm-specific factors play a vital part in deciding the capital structure of businesses and makes a significant contribution to the literature. Thus, the present study examines the drivers of the capital structure among sample Indian companies, which allow firm managers and regulators to recognise relevant variables that optimise performance. This study is limited to Indian companies and only firm-specific variables were considered.

The current research focuses on the impact of firm-specific variables upon the financing decisions of Indian companies. In the background of developed countries, numerous studies in this field have been carried out. In the Indian context, however, there are not many researches in this area. However, the existing studies use one or two ordinary least square (OLS) models, resulting in a lack of thorough research and robust results. To address this gap in the analysis, the current study used four models and used a 15-year time frame, as well as a bigger sample size, which was not used in earlier investigations.

Recognizing the severity of COVID-19 on the US economy, the authors investigate the behavior of US-listed firms towards leverage speed of adjustment (SOA) during the pandemic. While prior evidence (based on an international study) shows that firm leverage increased during the pandemic leading to a higher SOA toward leverage ratios, leverage for US firms during the same period reduced drastically. Yet there is a dearth of empirical studies on the behavior of US-listed firms' SOA during the pandemic. The authors fill this void.

The study includes US-listed non-financial and non-utility firms for the period 2015Q1-2021Q4, covering a total sample of 45,213 firm-quarter observations. The authors’ empirical strategy is based on the generalized method of moments (GMM) and firm-fixed effect methodology, controlling for firm- and quarter-fixed effects.

Three main findings are established: (1) while the SOA toward book target increased during the pandemic, SOA toward market target increased significantly only for less valued and cash-constrained firms; (2) firms in states most impacted by the pandemic adjusted faster towards target ratio; and (3) while the emergence of the pandemic and the overall firm-level risk increased (decreased) the deviation from book (market) target, firm-level risk partially mediated the effect of the pandemic on how far firms deviated from target ratio.

This study enhances our understanding of leverage adjustment during the crisis and shows that risk avoidance motive and the market value of firms are key determinants of convergence rate during the crisis and further demonstrates that market leverage is more sensitive to market dynamics. As such, caution must be taken when dealing with and interpreting market leverage SOA.

Although prior evidence based on international study provides insights into how firms behave toward their leverage ratios because of the pandemic, little is known about how US firms react to the pandemic in terms of the target ratios, particularly (1) since the USA is one of the severely affected countries and (2) firms in the USA reduced their leverage ratios as against what prior evidence shows. The authors provide evidence to explain how and why US firms reacted toward their SOA during the pandemic.

Pores and shrink holes are unavoidable defects in the die-casting mass production process which may significantly influence the strength, fatigue and fracture behaviour as well as the life span of structures, especially if they are subjected to high static and dynamic loads. Such defects should be considered during the design process or after production, where the defects could be detected with the help of computed tomography (CT) measurements. However, this is usually not done in today's mass production environments. This paper deals with the stress analysis of die-cast structural parts with pores found from CT measurements or that are artificially placed within a structure.

In this paper the authors illustrate two general methodologies to take into account the porosity of die-cast components in the stress analysis. The detailed geometry of a die-cast part including all discontinuities such as pores and shrink holes can be included via STL data provided by CT measurements. The first approach is a combination of the finite element method (FEM) and the finite cell method (FCM), which extends the FEM if the real geometry cuts finite elements. The FCM is only applied in regions with pores. This procedure has the advantage that all simulations with different pore distributions, real or artificial, can be calculated without changing the base finite element mesh. The second approach includes the pore information as STL data into the original CAD model and creates a new adapted finite element mesh for the simulation. Both methods are compared and evaluated for an industrial problem.

The STL data of defects which the authors received from CT measurements could not be directly applied without repairing them. Therefore, for FEM applications an appropriate repair procedure is proposed. The first approach, which combines the FEM with the FCM, the authors have realized within the commercial software tool Abaqus. This combination performs well, which is demonstrated for test examples, and is also applied for a complex industrial project. The developed in-house code still has some limitations which restrict broader application in industry. The second pure FEM-based approach works well without limitations but requires increasing computational effort if many different pore distributions are to be investigated.

A new simulation approach which combines the FEM with the FCM has been developed and implemented into the commercial Abaqus FEM software. This approach the authors have applied to simulate a real engineering die-cast structure with pores. This approach could become a preferred way to consider pores in practical applications, where the porosity can be derived either from CT measurements or are artificially adopted for design purposes. The authors have also shown how pores can be considered in the standard FEM analysis as well.

The case study offers a unique teaching tool to the instructor and learners. Very few cases offer a product and segmentation dilemma in a single problem. The discussion would enable learners to:

– conduct a situational analysis by using frameworks such as the 5C and SWOT;

– understand different kinds of segmentation options that a firm can consider;

– understand the nuances of making a viable and actionable new product launch decision;

– analyze the pros and cons of a segmentation decision and comprehend how the decision will impact the firm’s marketing and/or business strategy.

Sirona Hygiene Private Limited was a young startup founded in 2015 by Deep Bajaj. The firm had three brands under its umbrella, namely, female hygiene (Peebuddy), menstrual hygiene (Sirona) and protection and wellness (BodyGuard). Though the firm was recognized for feminine hygiene products, the pandemic boosted the sale of BodyGuard face masks and hand sanitizers.

The sanitizer market was growing, and protection and sanitization products were now part of every consumer’s daily ritual. As BodyGuard now had some brand recognition, Sirona could consider expanding the sanitizer line with a natural new product formulation. However, the expansion decision could have short- and long-term impacts on BodyGuard and Sirona Hygiene. The decision could be two-pronged, involving a product line expansion and revisiting the BodyGuard segmentation strategy. Currently, the BodyGuard range was focused on business-to-consumer (B2C) users, but volumes were higher in business-to-business (B2B). Second, BodyGuard was a forced fit brand amongst the Sirona family of feminine products

Thus, as Sirona considered a new product opportunity, assessing the viability of a possible move to the B2B segment may be prudent. However, the BodyGuard range also had mosquito repellents and baby products, which were essentially a B2C option, so was it more practical to stay as a B2C brand? Furthermore, if BodyGuard stayed a B2C brand, should it consider a demographic segmentation, or was a psychographic approach more beneficial in a cluttered commoditized space such as sanitizers? Which approach would build a consumer–brand connection? Or should the brand straddle both segments? Finally, the firm would also need to assess the BodyGuard segmentation strategy from the overarching Sirona business strategy.

The case can be used for a foundation course in Marketing and/or an advanced elective on Product Management or Marketing Strategy.

Teaching notes are available for educators only.

CSS 8: Marketing.

This paper aims to test for volatility spillovers among Islamic stock markets with the exogenous impact of geopolitical risk (GPR) to check the risk transmission among Saudi Arabia, Malaysia, Indonesia and Turkey. Researchers test for both the symmetric and asymmetric risk transmission.

For the symmetric response of volatility, the study uses simple generalized autoregressive conditional heteroscedastic (GARCH) and for the asymmetric response of volatility with the exogenous impact of GPR, the exponential GARCH models have been adopted.

The results suggest spillover effects exist from Turkey to Saudi Arabia, Indonesia to Malaysia and Saudi Arabia and Malaysia to Indonesia. The findings of volatility spillover from GPR to sample countries suggest that only Malaysia and Indonesia experience volatility spillovers from GPR.

The present study is limited to the context of four countries and Islamic equities; the study contributes to the literature on volatility spillover, Islamic finance, GPR and asset pricing.

This study contributes to individual, institutional investors’ policymakers’ knowledge in determining security prices, trading plans, investment hedging and policy regulation.

The extant literature disregards the GPR index to examine the volatility spillover effects among Islamic stock markets, which allow researchers to justify the mechanism of risk transmission due to GPR across the Islamic stock market.

To the best of the authors’ knowledge, this is the first research of its type to look at volatility spillover and GPR transmission in Islamic stock markets.

Allocation of the marketing budget has become increasingly challenging due to the diverse channel exposure to customers. This study aims to enhance global marketing knowledge by introducing an ensemble attribution model to optimize marketing budget allocation for online marketing channels. As empirical research, this study demonstrates the supremacy of the ensemble model over standalone models.

The transactional data set for car insurance from an Indian insurance aggregator is used in this empirical study. The data set contains information from more than three million platform visitors. A robust ensemble model is created by combining results from two probabilistic models, namely, the Markov chain model and the Shapley value. These results are compared and validated with heuristic models. Also, the performances of online marketing channels and attribution models are evaluated based on the devices used (i.e. desktop vs mobile).

Channel importance charts for desktop and mobile devices are analyzed to understand the top contributing online marketing channels. Customer relationship management-emailers and Google cost per click a paid advertising is identified as the top two marketing channels for desktop and mobile channels. The research reveals that ensemble model accuracy is better than the standalone model, that is, the Markov chain model and the Shapley value.

To the best of the authors’ knowledge, the current research is the first of its kind to introduce ensemble modeling for solving attribution problems in online marketing. A comparison with heuristic models using different devices (desktop and mobile) offers insights into the results with heuristic models.