Search results

The purpose of this study is to identify and validate the key consumer-related factors influencing consumer attitude toward electric vehicles (EVs) and intention to purchase EVs. The role of consumer attitude in mediating consumer-related factors and intention to buy is also explored in this study. Consumer-related factors considered in this study include environmental orientation (EO), new technology orientation (NTO), social orientation (SO) and perceived monetary benefits (PMB).

Data for the study were collected from individuals who do not own an EV in Chennai, Tamil Nadu, India. Data were collected from 388 respondents using Google Forms. Convenience sampling technique was used to identify the respondents. Both descriptive and inferential statistics techniques were used to analyze data collected.

The study identified EO, SO and PMB as antecedents of attitude (ATT) toward EV and NTO, SO and PMB as potential drivers of EV purchase intention (PI). In addition, the authors also observe that EO is fully mediated by ATT toward consumer PI, whereas ATT partially mediates the relationship between the independent variables SO and PMB and the dependent variable PI.

EVs are at the forefront of automotive innovation. Automobile industries producing EVs showcase a commitment to embracing cutting-edge technology, positioning their companies as forward-thinking and technologically advanced. Creating awareness about EV technology, dispelling myths about range anxiety and charging times should be done methodologically. The lower operating costs of EVs and the government policies that reduce the total cost of ownership can lead to faster market penetration. The widespread adoption of EVs can contribute to a nation’s economic growth by adding new businesses in the industry, infrastructure development and job creation.

The reduced environmental impact of EVs, such as improved air quality and reduced greenhouse gas emissions, can lead to significant societal benefits in terms of better human health and mitigating the effects of climate change. Governments can leverage these benefits to achieve climate targets and improve public health.

Studies about EV diffusion and adoption in India are yet to gain momentum considering the slow penetration of EVs in the Indian automobile market. The Indian Government is enforcing policies and incentives to accelerate the adoption of EVs. However, without understanding the drivers of consumer EV adoption, it is difficult for policymakers to accelerate the growth of the EV market. The present study is one step toward understanding the same in its original sense.

Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.

Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.

Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.

Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.

In clinical analysis, medical image segmentation is an important step to study the anatomical structure. This helps to diagnose and classify abnormality in the image. The wide variations in the image modality and limitations in the acquisition process of instruments make this segmentation challenging. This paper aims to propose a semi-automatic model to tackle these challenges and to segment medical images.

The authors propose Legendre polynomial-based active contour to segment region of interest (ROI) from the noisy, low-resolution and inhomogeneous medical images using the soft computing and multi-resolution framework. In the first phase, initial segmentation (i.e. prior clustering) is obtained from low-resolution medical images using fuzzy C-mean (FCM) clustering and noise is suppressed using wavelet energy-based multi-resolution approach. In the second phase, resultant segmentation is obtained using the Legendre polynomial-based level set approach.

The proposed model is tested on different medical images such as x-ray images for brain tumor identification, magnetic resonance imaging (MRI), spine images, blood cells and blood vessels. The rigorous analysis of the model is carried out by calculating the improvement against noise, required processing time and accuracy of the segmentation. The comparative analysis concludes that the proposed model withstands the noise and succeeds to segment any type of medical modality achieving an average accuracy of 99.57%.

The proposed design is an improvement to the Legendre level set (L2S) model. The integration of FCM and wavelet transform in L2S makes model insensitive to noise and intensity inhomogeneity and hence it succeeds to segment ROI from a wide variety of medical images even for the images where L2S failed to segment them.

The purpose of this paper is to experimentally investigate the effect of aluminium oxide (Al₂O₃) nanoparticles on gear oil (SAE EP 90) as a lubricant in heavy earth moving machinery (HEMM).

Particle size distribution, viscosity, density, stability and other rheological properties have been measured. The variations in rheological properties with varying nanoparticle volume fraction and temperature have been investigated at atmospheric pressure over a temperature range of 15-40°C. Classical as well as modified Krieger – Dougherty models have been used for finding out viscosity variation and a new empirical model has been presented.

Dynamic light scattering data confirm the presence of large agglomeration of about 5.5 times of primary nanoparticles in nanofluid. Nanofluid starts behaving as a non-Newtonian fluid with increasing nanoparticle volume fraction. Viscosity of nanofluid is enhanced by 1.7 times of base fluid with 2 per cent volume fraction of Al₂O₃ nanoparticles, while it significantly decreases with increase in temperature. The stability of nanofluid decreases with increase in nanoparticle volume fraction due to settling down of nanoparticles. It has also been observed that shear thinning increases with increasing nanoparticle volume fraction.

It is expected that these findings will contribute towards the improvement in rheological and thermal properties of the conventional lubricants used in HEMM. The outcome may help the designers, researchers and manufacturers of the HEMM.

Most of the previous research in this field is confined with base fluid as water, ethylene glycol, transformer oil, etc. Gear oil in HEMM performs under high mechanical and thermal load. The Al₂O₃/gear oil nanofluid is expected to have better cooling and lubrication properties.

The purpose of this paper is to investigate the inhibitive performance of Coreopsis tinctoria (C. tinctoria) plant extract for the corrosion of mild steel in 0.5 M H₂SO₄.

The inhibition efficiency was studied by weight loss, electrochemical measurements and the surface analysis was done by Raman, scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM-EDS) and atomic absorption spectroscopy (AAS) analysis.

Maximum inhibition efficiency of C. tinctoria in 0.5 M H₂SO₄ on mild steel is 80.62 per cent (500 ppm) at 303 ± 1K. The adsorption of the C. tinctoria on the mild steel surface in 0.5 M H₂SO₄ was found to obey Langmuir adsorption isotherm. Temperature studies were carried out and the significant parameters, such as change in enthalpy (ΔH^°), change in entropy (ΔS^°) and change in free energy (ΔG°_ads) and heat of adsorption (Qads), were calculated. The productive layer formed on the mild steel surface in 0.5 M H₂SO₄ were confirmed by the Raman spectral analysis.

This paper provides information on the inhibitive properties of C. tinctoria plant extract which is found to be a good corrosion inhibitor for mild steel in 0.5 M H₂SO₄.

This study aims to enhance heat transfer efficiency while minimizing friction factor and entropy generation in the flow of Nickel zinc ferrite (NiZnFe₂O₄) nanoparticles suspended in multigrade 20W-40 motor oil (as specified by the Society of Automotive Engineers). The investigation focuses on the effects of the melting process, nonspherical particle shapes, thermal dispersion and viscous dissipation on the nanofluid flow.

The fundamental governing equations are transformed into a set of similarity equations using Lie group transformations. The resulting set of equations is numerically solved using the spectral local linearization method. Additionally, sensitivity analysis using response surface methodology (RSM) is conducted to evaluate the influence of key parameters on response function.

Higher dispersion reduces entropy production. Needle-shaped particles significantly enhance heat transfer by 27.65% with melting and reduce entropy generation by 45.32%. Increasing the Darcy number results in a reduction of friction by 16.06%, lower entropy by 31.72% and an increase in heat transfer by 17.26%. The Nusselt number is highly sensitive to thermal dispersion across melting and varying volume fraction parameters.

This study addresses a significant research gap by exploring the combined effects of melting, particle shapes and thermal dispersion on nanofluid flow, which has not been thoroughly investigated before. The focus on practical applications such as fuel cells, material processing, biomedicine and various cooling systems underscores its relevance to sectors such as nuclear reactors, tumor treatments and manufacturing. The incorporation of RSM for friction factor analysis introduces a unique dimension to the research, offering novel insights into optimizing nanofluid performance under diverse conditions.

The purpose of this paper is to report the results of an investigation into individual investors' perceptions of the factors affecting buying, holding and selling of stock on the Bahrain stock exchange (BSE). Additionally, the paper investigates the perceptions of individual investors about corporate financial statements as a source of information for individual investors' investment decisions and what specific information such investors would like firms to disclose in these reports.

The research method involved a mail questionnaire sent to 800 individual investors. The response rate was 42.6 percent. This research method was complemented by a series of field interviews conducted with 20 investors and six stockbrokers for the purpose of gaining additional insights into the topic.

The study found that individual investors perceived corporate financial statements as the most important source of information for their investment decisions. The results also show a relatively high degree of agreement within the groups (both large and small) as to the ranking in terms of the importance of the topics. Overall, the study found relatively high levels of consensus between the two user‐groups with regards to the majority of questions investigated. The greatest difference between the user‐groups regards the perception of the relative importance of the cash‐flow statement, the income statement and which information items are needed for investors' decision making.

The paper offers rich data on the perceptions and uses of financial and non‐financial information by individual investors. This is the first time this type of research has been conducted in Bahrain.

The purpose of this study is to develop a research agenda on internal controls for offshored accounting processes. It further develops a linkage between internal controls of offshored accounting processes and auditing of the organization. Offshoring of accounting processes has become a common business practice, pursued by firms to reduce costs and focus on core competencies. However, our understanding about internal controls of these offshored processes is limited.

Grounded in theory that is supported by prior literature and interviews with practitioners, this paper attempts to develop a research agenda on internal controls for offshored accounting processes.

The main findings of our study suggest that while offshoring saves costs and allows the clients to focus on their core competencies, it also poses risks to the clients’ organizations. To mitigate these risks and comply with the regulatory requirements of the countries where the clients are located, clients and their offshore vendors need to effectively establish adequate internal controls for offshored business processes. Clients should seek those vendors who have appropriate processes in place and are willing to provide Service Organization Control (SOC) reports (or at least are capable of getting a SOC report in the near future). Moreover, clients should avoid offshoring the processes that would exist in defective internal control systems. Similarly, vendors should avoid undertaking those processes for which they are incapable of maintaining efficient internal controls.

Our study has implications for academicians as well as practitioners on understanding the determinants and consequences of internal control for offshored processes.

While internal controls for offshored accounting process and related regulatory changes have been increasingly important topics, little research has been devoted to explore their implications on accounting and auditing literature. We attempt to bridge this gap by synthesizing prior research on internal controls and auditing, and further developing a set of research questions for academic research. Our hope is to spur a new area of research that has not been explored before.

The purpose of this paper is to investigate the perceptions of auditor independence between auditors, bank‐loan officers, and financial analysts in Bahrain.

This study examines the effect of 41 independence‐enhancing and – threatening Factors on the perceptions of auditor, bank‐loan officers, and financial analysts regarding auditor independence in Bahrain. Out of 450 questionnaires distributed, 281 usable responses were received, representing a response rate of 62.4 percent.

Overall, the three groups agree on the classification of the 41 factors into two groups; however, they do not agree on the relative importance of those factors on their perception of auditor independence. Economic reliance of auditors on their clients and the provisions of non‐audit service, competition, and long tenure of audit services are considered the most important independence‐threatening factors. The risks posed to auditors in fulfilling their audit engagement, regulatory rights and requirements surrounding auditor change, regulation concerning the appointment/remuneration of auditors, and the disclosure of financial and nonfinancial relationships are among the most important factors that are perceived by the three groups to enhance auditor independence.

The samples did not include all users of financial statements; the samples were drawn only from institutions that were willing to take part, and consequently the results might not be applicable to those that did not take part in the study; and data were collected using a survey questionnaire and this approach is subject to certain types of bias such as response bias, which may affect the reliability of the respondents' answers.

The paper can inform policy makers, governments, and professional accounting bodies in emerging markets in countries that share similar economic, political, and cultural environment on how policies and frameworks related to auditor independence can be structured to ensure adequate regulation of the capital market, and enhance the awareness of users and auditors about the contextual factors surrounding the role of an auditor, in addition to the possible threats and enhancing factors that affect auditor independence.

The paper offers rich data on the perceptions of auditors' independence of auditors and users of financial statements. This is the first time, this type of research has been conducted in Bahrain.

Predicting the stock market's prices has always been an interesting topic since its closely related to making money. Recently, the advances in natural language processing (NLP) have opened new perspectives for solving this task. The purpose of this paper is to show a state-of-the-art natural language approach to using language in predicting the stock market.

In this paper, the conventional statistical models for time-series prediction are implemented as a benchmark. Then, for methodological comparison, various state-of-the-art natural language models ranging from the baseline convolutional and recurrent neural network models to the most advanced transformer-based models are developed, implemented and tested.

Experimental results show that there is a correlation between the textual information in the news headlines and stock price prediction. The model based on the GRU (gated recurrent unit) cell with one linear layer, which takes pairs of the historical prices and the sentiment score calculated using transformer-based models, achieved the best result.

This study provides an insight into how to use NLP to improve stock price prediction and shows that there is a correlation between news headlines and stock price prediction.