Barnali Chaklader and B. Padmapriya
Building on pecking order theory, this study seeks to understand the various financial factors that influence top management's decision regarding the company’s capital structure…
Abstract
Purpose
Building on pecking order theory, this study seeks to understand the various financial factors that influence top management's decision regarding the company’s capital structure. The authors attempt to understand and analyse whether the capital structure of mid‐ and small‐cap firms is affected by cash surplus scaled to total assets. Along with other determinants of capital structure such as liquidity, profitability, tangibility, market capitalisation and age, this is considered one of the major factors. Cash surplus is calculated using data from the cash flow statement. It is defined as the difference in cash from operating activities and that from investing activities and is scaled to total assets. To the best of the authors’ knowledge, this is the first study to regress cash surplus scaled to total assets and other determinants over leverage to examine the impact on mid‐ and small‐cap firms. The pecking order theory was found to hold for firms earning cash surplus.
Design/methodology/approach
Data were collected from the CMIE Prowess database of all firms listed on the NIFTY Small cap 250 index and NIFTY Midcap 150 index. The data of non-financial firms belonging to the midcap and small-cap sector, listed on the National Stock Exchange of India from 2012 to 2019 were considered. After cleaning the data, an unbalanced panel of 171 companies totalling 1,362 observations for the NIFTY Small-cap 250 index and another panel of 96 companies with 761 observations for the NIFTY Midcap 150 index was created. Panel data regression analysis was used to determine the effect of cash surplus scaled to total assets on the firms' capital structure.
Findings
This study demonstrates how small- and midcap firms' behave differently in taking capital structure decisions. Pecking order theory was found to hold for firms earning cash surplus as a proportion of total assets (Surplusta).
Research limitations/implications
The study was conducted through data available on secondary sources and database. The study can be better conducted by conducting a primary survey too. Further study may be conducted with a blend of secondary and questionnaire method. The results can be compared to check the similarity in findings.
Practical implications
Managers can benefit from the findings when making decisions on long- and short-term loans. This study can help managers in terms of the financial variables that have a role to play in the financial leverage of the company. The decision of the managers of midcap or small-cap firms would be different. Factors influencing short- and long-term borrowings are different. Academics can discuss whether there is any difference in the influence of capital structure variables of small- and midcap companies and the reasons for such differences. Judicious decisions on capital structure will create wealth for the shareholders as the right decision about leverage would result in a proper cost of capital. The findings also add to the existing literature on the Pecking order theory.
Social implications
Academics can discuss whether there is any difference in the influence of capital structure variables of small- and midcap companies and the reasons for such differences.
Originality/value
The study extends the existing literature by demonstrating that the capital structure of mid and small-cap firms is affected by cash surplus scaled to total assets. The pecking order theory was found to hold for firms earning cash surplus. This study can inform the practitioners about the financial variables that have a role to play in the company's financial leverage. As the results and significance of the variables of the midcap or small-cap firms are different, the decisions of the managers of these firms would be separate for the capital structure of their firms. The study also infers that the factors influencing short and long-term borrowings are different. The study determines whether managers' decision-making in such companies is different in terms of raising short- and long-term loans. The study attempts to guide managers in considering the different variables that would influence their capital structure decisions, particularly the decision to include debt in the capital. Financial variables need not be of equal importance for managers belonging to small- and midcap companies.
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Ravindra Nath Shukla, Vishal Vyas and Animesh Chaturvedi
We aim to analyze the capital structure heterogeneity for manufacturing and service sector firms. Additionally, we analyze the impact of the COVID-19 pandemic on the leverage…
Abstract
Purpose
We aim to analyze the capital structure heterogeneity for manufacturing and service sector firms. Additionally, we analyze the impact of the COVID-19 pandemic on the leverage adjustments of corporate firms.
Design/methodology/approach
This study applies the two-step system generalized method of moments (system-GMM) and panel data of 1,115 manufacturing and 482 service sector firms listed with the Bombay Stock Exchange (S&P BSE) from 2010 to 2023. We developed and analyzed three models. Model 1 analyzes the leverage determinants and speed of adjustment (SOA) for the manufacturing and service sectors. Model 2 evaluates the leverage SOA for various sub-sectors, and Model 3 analyzes the impact of the COVID-19 pandemic on the leverage SOA.
Findings
This study suggests the three following. First, the direction of leverage determinants suggests that manufacturing firms are highly tangible. In contrast, service sector firms are high-growth firms and recorded a higher SOA (12.01%) than manufacturing (9.09%). Second, analyzing the leverage heterogeneity, we found that SOA varies across the sub-sectors. For manufacturing, food and beverage sub-sector recorded the highest SOA (12.58%), while consumer durables reported the lowest (6.38%). Communication recorded the highest (24.15%) for services, while industrial services recorded the lowest (11.18%). Third, firms across sectors and sub-sectors increased their SOA during COVID-19 pandemic.
Research limitations/implications
This in-depth analysis of leverage heterogeneity for different sectors and subsectors will assist policymakers, corporate managers and other stakeholders in making agile financial decisions.
Originality/value
The analysis of leverage heterogeneity for the manufacturing and service sector from the emerging Indian economy marks a novel contribution to existing literature.
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Mohamed M. El-Dyasty and Ahmed Elamer
This study examines the impact of female directors on cash holdings in Egyptian listed firms, particularly in light of Decree 123/2019, which mandates female board representation…
Abstract
Purpose
This study examines the impact of female directors on cash holdings in Egyptian listed firms, particularly in light of Decree 123/2019, which mandates female board representation. This study aims to determine if female directors mitigate agency conflicts related to cash holdings and how these dynamics shift post-quota implementation.
Design/methodology/approach
Using a panel fixed-effects model, the research analyzes 1,563 firm-year observations from 223 non-financial Egyptian firms listed on the EGX between 2014 and 2022. The robustness of the findings is tested through additional analyses using alternative proxies for cash holdings, different sample periods and a two-stage least squares approach to address endogeneity concerns.
Findings
This study finds a significant negative association between female directors and cash holdings, suggesting that female board members may promote more conservative cash management practices. However, this relationship weakens post-quota implementation, becoming statistically insignificant. This implies that while quotas increase female representation, they do not necessarily enhance corporate governance effectiveness regarding cash management. The pre-quota positive link between female directors and excess cash holdings also becomes insignificant post-quota.
Research limitations/implications
The study focuses on female directors’ impact on cash holdings, excluding potential effects on other board subcommittees or functions. It does not capture long-term benefits of increased female representation, which may emerge as the pool of qualified female directors grows. Future research should explore broader implications of gender diversity guidelines and other diversity dimensions across various corporate governance aspects and institutional contexts.
Originality/value
This research provides empirical evidence from an emerging market context on the understudied impact of gender diversity on cash holdings. It critically evaluates the unintended consequences of mandatory gender quotas, highlighting the complexity of regulatory interventions in corporate governance. The study stresses the need for policymakers to address factors limiting the effectiveness of such quotas and to consider potential suboptimal outcomes when increasing female board representation without a corresponding increase in the supply of qualified female directors.
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Barnali Chaklader, Garima Chaklader and Santosh Kumar Shrivastav
This study thoroughly examines the relationship between environmental, social and governance (ESG) scores and their subcategories with the investment decisions made by foreign…
Abstract
Purpose
This study thoroughly examines the relationship between environmental, social and governance (ESG) scores and their subcategories with the investment decisions made by foreign institutional investors (FII). These subcategories include resource use, emission reduction and innovation under the environmental pillar, workforce, human rights, community and product responsibility under the social pillar and management, shareholders and CSR strategy under the governance pillar.
Design/methodology/approach
A machine learning technique known as “topic modeling” is used to analyse the current literature on ESG. To investigate the correlation between ESG scores and their subcategories with the investment decisions made by FII and to address concerns regarding multicollinearity and overfitting, a penalty-based regression model is employed.
Findings
The findings indicate that FIIs invest in firms with higher emission reduction and innovation scores under the environmental indicator. Additionally, firms with high human rights, community and product responsibility scores under the social indicator category have a positive relationship with FII investors. All subcategories of governance indicators, such as corporate social responsibility (CSR), strategy, shareholders and management scores, also positively impact FII investment. Of the three indicators, i.e. ESG, non-promoter FIIs give maximum weightage to governance indicators.
Research limitations/implications
Since ESG is a contemporary topic, the findings on the relationship between different categories of ESG on FII investment will support managers in their FII investment. Also, the study will help the government frame policy decisions on ESG.
Originality/value
Previous studies have explored the impact of the overall ESG indicators on FII investments, but they have not specifically studied the influence of sub-indicators within these categories on investment decisions. By addressing this gap, the study enhances stakeholder theory by identifying and prioritizing the various subcategories of ESG indicators that impact FII investment decisions.
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Haifeng Fang, Yangyang Wei and Shuo Dong
Tactile sensation is an important sensory function for robots in contact with the external environment. To better acquire tactile information about objects, this paper aims to…
Abstract
Purpose
Tactile sensation is an important sensory function for robots in contact with the external environment. To better acquire tactile information about objects, this paper aims to propose a three-layer structure of the interdigital flexible tactile sensor.
Design/methodology/approach
The sensor consists of a bottom electrode layer, a middle pressure-sensitive layer and a top indenter layer. First, the pressure sensitive material, structure design, fabrication process and circuit design of the sensor are introduced. Then, the calibration and performance test of the designed sensor is carried out. Four functions are used to fit and calibrate the relationship between the output voltage of the sensor and the contact force. Finally, the contact force sensing test of different weight objects and the flexible test of the sensor are carried out.
Findings
The performance test results show that the sensitivity of the sensor is 0.93 V/N when it is loaded with 0–3 N and 0.23 V/N when it is loaded with 3–5 N. It shows good repeatability, and the cross-interference between the sensing units is generally low. The contact force sensing test results of different weight objects show that the proposed sensor performs well in contact force. Each part of the sensor is a flexible material, allowing the sensor to achieve bending deformation, so that the sensor can better perceive the contact signs of the grasped object.
Practical implications
The sensor can paste the surface of the paper robot’s gripper to measure the contact force of the grasping object and estimate the contour of the object.
Originality/value
In this paper, a three-layer interdigital flexible tactile sensor is proposed, and the structural parameters of the interdigital electrode are designed to improve the sensitivity and response speed of the sensor. The indenter with three shapes of the prism, square cylinder and hemisphere is preliminarily designed and the prism indenter with better conduction force is selected through finite element analysis, which can concentrate the external force in the sensing area to improve the sensitivity. The sensor designed in this paper can realize the measurement of contact force, which provides a certain reference for the field of robot tactile.
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Sudhaman Parthasarathy and S.T. Padmapriya
Algorithm bias refers to repetitive computer program errors that give some users more weight than others. The aim of this article is to provide a deeper insight of algorithm bias…
Abstract
Purpose
Algorithm bias refers to repetitive computer program errors that give some users more weight than others. The aim of this article is to provide a deeper insight of algorithm bias in AI-enabled ERP software customization. Although algorithmic bias in machine learning models has uneven, unfair and unjust impacts, research on it is mostly anecdotal and scattered.
Design/methodology/approach
As guided by the previous research (Akter et al., 2022), this study presents the possible design bias (model, data and method) one may experience with enterprise resource planning (ERP) software customization algorithm. This study then presents the artificial intelligence (AI) version of ERP customization algorithm using k-nearest neighbours algorithm.
Findings
This study illustrates the possible bias when the prioritized requirements customization estimation (PRCE) algorithm available in the ERP literature is executed without any AI. Then, the authors present their newly developed AI version of the PRCE algorithm that uses ML techniques. The authors then discuss its adjoining algorithmic bias with an illustration. Further, the authors also draw a roadmap for managing algorithmic bias during ERP customization in practice.
Originality/value
To the best of the authors’ knowledge, no prior research has attempted to understand the algorithmic bias that occurs during the execution of the ERP customization algorithm (with or without AI).
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Padmapriya Nammalwar, Ovidiu Ghita and Paul F. Whelan
The purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be applied to…
Abstract
Purpose
The purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be applied to any real‐world applications for appropriate interpretation.
Design/methodology/approach
The framework derives the contributions of colour and texture in image segmentation. Local binary pattern and an unsupervised k‐means clustering are used to cluster pixels in the chrominance plane. An unsupervised segmentation method is adopted. A quantitative estimation of colour and texture performance in segmentation is presented. The proposed method is tested using different mosaic and natural images and other image database used in computer vision. The framework is applied to three different applications namely, Irish script on screen images, skin cancer images and sediment profile imagery to demonstrate the robustness of the framework.
Findings
The inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicate that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient.
Originality/value
The novelty lies in the development of a generic framework using both colour and texture features for image segmentation and the different applications from various fields.
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This study seeks to explore the underlying benefits and constraints of travel among pregnant women. This study deploys a series of in-depth interviews involving 12 participants…
Abstract
This study seeks to explore the underlying benefits and constraints of travel among pregnant women. This study deploys a series of in-depth interviews involving 12 participants. The findings show that emotional refreshment and health promotion are the two benefits of travel expressed by pregnant travelers, while physical discomfort is an unavoidable constraint of travel. To lessen the obstacle of the trip, according to the constraint identified, this study suggests that service providers actively look into the particular needs of pregnant travelers, such as comfortable restrooms. Lastly, it gives a suggestion for future studies on the issues in connection with the examination of pregnant women's travel experiences in each trimester.
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In this work, a numerical algorithm is presented for stability analysis of cold-formed steel (CFS) channel sections.
Abstract
Purpose
In this work, a numerical algorithm is presented for stability analysis of cold-formed steel (CFS) channel sections.
Design/methodology/approach
A nonlinear optimization problem is formulated using energy-based technique of idealized channel section subject shear, compression and biaxial bending. The total potential energy is minimized with respect to skew angle and half wavelength of the buckling mode. The optimization algorithm is updated sequentially using quadratic approximation until minimum buckling coefficient is attained. The developed algorithm is validated using other numerical techniques.
Findings
The described algorithm is computationally effective and can be utilized in the industry for analysis of CFS channels under any load combination.
Practical implications
The paper offers a new tool for engineers in practice to analyze channels subject to combined loadings.
Originality/value
Very limited literature dealt with the stability of channels under combined loading. A new numerical algorithm is provided to practitioners to utilize in the industry for analysis of channel sections under combined loading. Unlike finite element or finite strip methods, the channel is not discretized into subelements. Mathematical programming technique is used to find the buckling load. Parametric studies are then carried out to highlight influences of geometric interaction of the channel components and to provide useful guidance to the design of CFS channels.
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Michael Wayne Davidson, John Parnell and Shaun Wesley Davenport
The purpose of this study is to address a critical gap in enterprise resource planning (ERP) implementation process for small and medium-sized enterprises (SMEs) by acknowledging…
Abstract
Purpose
The purpose of this study is to address a critical gap in enterprise resource planning (ERP) implementation process for small and medium-sized enterprises (SMEs) by acknowledging and countering cognitive biases through a cognitive bias awareness matrix model. Cognitive biases such as temporal discounting and optimism bias often skew decision-making, leading SMEs to prioritize short-term benefits over long-term sustainability or underestimate the challenges involved in ERP implementation. These biases can result in costly missteps, underutilizing ERP systems and project failure. This study enhances decision-making processes in ERP adoption by introducing a matrix that allows SMEs to self-assess their level of awareness and proactivity when addressing cognitive biases in decision-making.
Design/methodology/approach
The design and methodology of this research involves a structured approach using the problem-intervention-comparison-outcome-context (PICOC) framework to systematically explore the influence of cognitive biases on ERP decision-making in SMEs. The study integrates a comprehensive literature review, empirical data analysis and case studies to develop the Cognitive Bias Awareness Matrix. This matrix enables SMEs to self-assess their susceptibility to biases like temporal discounting and optimism bias, promoting proactive strategies for more informed ERP decision-making. The approach is designed to enhance SMEs’ awareness and management of cognitive biases, aiming to improve ERP implementation success rates and operational efficiency.
Findings
The findings underscore the profound impact of cognitive biases and information asymmetry on ERP system selection and implementation in SMEs. Temporal discounting often leads decision-makers to favor immediate cost-saving solutions, potentially resulting in higher long-term expenses due to the lack of scalability. Optimism bias tends to cause underestimating risks and overestimating benefits, leading to insufficient planning and resource allocation. Furthermore, information asymmetry between ERP vendors and SME decision-makers exacerbates these biases, steering choices toward options that may not fully align with the SME’s long-term interests.
Research limitations/implications
The study’s primary limitation is its concentrated focus on temporal discounting and optimism bias, potentially overlooking other cognitive biases that could impact ERP decision-making in SMEs. The PICOC framework, while structuring the research effectively, may restrict the exploration of broader organizational and technological factors influencing ERP success. Future research should expand the range of cognitive biases and explore additional variables within the ERP implementation process. Incorporating a broader array of behavioral economic principles and conducting longitudinal studies could provide a more comprehensive understanding of the challenges and dynamics in ERP adoption and utilization in SMEs.
Practical implications
The practical implications of this study are significant for SMEs implementing ERP systems. By adopting the Cognitive Bias Awareness Matrix, SMEs can identify and mitigate cognitive biases like temporal discounting and optimism bias, leading to more rational and effective decision-making. This tool enables SMEs to shift focus from short-term gains to long-term strategic benefits, improving ERP system selection, implementation and utilization. Regular use of the matrix can help prevent costly implementation errors and enhance operational efficiency. Additionally, training programs designed around the matrix can equip SME personnel with the skills to recognize and address biases, fostering a culture of informed decision-making.
Social implications
The study underscores significant social implications by enhancing decision-making within SMEs through cognitive bias awareness. By mitigating biases like temporal discounting and optimism bias, SMEs can make more socially responsible decisions, aligning their business practices with long-term sustainability and ethical standards. This shift improves operational outcomes and promotes a culture of accountability and transparency. The widespread adoption of the Cognitive Bias Awareness Matrix can lead to a more ethical business environment, where decisions are made with a deeper understanding of their long-term impacts on employees, customers and the broader community, fostering trust and sustainability in the business ecosystem.
Originality/value
This research introduces the original concept of the Cognitive Bias Awareness Matrix, a novel tool designed specifically for SMEs to evaluate and mitigate cognitive biases in ERP decision-making. This matrix fills a critical gap in the existing literature by providing a structured, actionable framework that effectively empowers SMEs to recognize and address biases such as temporal discounting and optimism bias. Its practical application promises to enhance decision-making processes and increase the success rates of ERP implementations. This contribution is valuable to behavioral economics and information systems, offering a unique approach to integrating cognitive insights into business technology strategies.