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This paper aims to develop a corporate financial distress model for Indian listed companies using financial and non-financial parameters by using a conditional logit regression technique.

This study used a sample of 96 companies, of which 48 were declared sick between 2014 and 2016. The sample was divided into a training sample and a testing sample. The variables for the study included nine financial variables and four non-financial variables. The models were developed using financial variables alone as well as combining financial and non-financial variables. The performance of the test sample was measured with confusion matrix, sensitivity, specificity, precision, F-measure, Types 1 and 2 error.

The results show that models with financial variables had a prediction accuracy of 85.19 and 86.11 per cent, whereas models with a combination of financial and non-financial variables predict with comparatively better accuracy of 89.81 and 91.67 per cent. Net asset value, long-term debt–equity ratio, return on investment, retention ratio, age, promoters holdings pledged and institutional holdings are the critical financial and non-financial predictors of financial distress.

This study contributes to the financial distress prediction literature in different ways. First, there have been, until now, few studies in the area of financial distress prediction in the Indian context. Second, business failure studies in the past have used only financial variables. The authors have combined financial and non-financial variables in their model to increase predictive ability. Thirdly, in most earlier studies, variable institutional holdings were found to affect financial distress negatively. In contrast, the authors found this parameter to be positively significant to the financial distress of the company. Finally, there have hitherto been few studies that have used promoter holdings pledged (PHP) or pledge ratio. The authors found this variable to influence business failure positively.

The purpose of this paper is to design organization message content strategies and analyse their information diffusion on the microblogging website, Twitter.

Using data from 29 brands and 9392 tweets, message strategies on twitter are classified into four strategies. Using content analysis all the tweets are classified into informational strategy, transformational strategy, interactional strategy and promotional strategy. Additionally, the information diffusion for the developed message strategies was explored. Furthermore, message content features such as text readability features, language features, Twitter-specific features, vividness features on information diffusion are analysed across message strategies. Additionally, the interaction between message strategies and message features was carried out.

Finding reveals that informational strategies were the dominant message strategy on Twitter. The influence of text readability features language features, Twitter-specific features, vividness features that influenced information diffusion varied across four message strategies.

This study offers a completely novel way for effectively analysing information diffusion for branded tweets on Twitter and can show a path to both researchers and practitioners for the development of successful social media marketing strategies.

The purpose of this paper is to study numerically the influence of a magnetic field on the transient free convective boundary layer flow of a nanofluid over a moving semi-infinite vertical cylinder with heat transfer

The problem is governed by the coupled non-linear partial differential equations with appropriate boundary conditions. The fluid is a water-based nanofluid containing nanoparticles of copper. The Brinkman model for dynamic viscosity and Maxwell–Garnett model for thermal conductivity are used. The governing boundary layer equations are written according to The Tiwari–Das nanofluid model. A robust, well-tested, implicit finite difference method of Crank–Nicolson type, which is unconditionally stable and convergent, is used to find the numerical solutions of the problem. The velocity and temperature profiles are studied for significant physical parameters such as the magnetic parameter, nanoparticles volume fraction and the thermal Grashof number Gr. The local skin-friction coefficient and the Nusselt number are also analysed and presented graphically.

The present computations have shown that an increase in the values of either magnetic parameter M or nanoparticle volume fraction decreases the local skin-friction coefficient, whereas the opposite effect is observed for thermal Grashof number Gr. The local Nusselt number increases with a rise in Gr and ϕ values. But an increase in M reduces the local Nusselt number.

This paper is relatively original and presents numerical investigation of transient two-dimensional laminar boundary layer free convective flow of a nanofluid over a moving semi-infinite vertical cylinder in the presence of an applied magnetic field. The present study is of immediate application to all those processes which are highly affected by heat enhancement concept and a magnetic field. Further the present study is relevant to nanofluid materials processing, chemical engineering coating operations exploiting nanomaterials and others.

Credit risk assessment has gained importance in recent years due to global financial crisis and credit crunch. Financial institutions therefore seek the support of credit rating agencies to predict the ability of creditors to meet financial persuasions. The purpose of this paper is to construct neural network (NN) and fuzzy support vector machine (FSVM) classifiers to discriminate good creditors from bad ones and identify a best classifier for credit risk assessment.

This study uses artificial neural network, the most popular AI technique used in the field of financial applications for classification and prediction and the new machine learning classification algorithm, FSVM to differentiate good creditors from bad. As membership value on data points influence the classification problem, this paper presents the new FSVM model. The instances membership is computed using fuzzy c-means by evolving a new membership. The FSVM model is also tested on different kernels and compared and the classifier with highest classification accuracy for a kernel is identified.

The paper identifies a standard AI model by comparing the performances of the NN model and FSVM model for a credit risk data set. This work proves that that FSVM model performs better than back propagation-neural network.

The proposed model can be used by financial institutions to accurately assess the credit risk pattern of customers and make better decisions.

This paper has developed a new membership for data points and has proposed a new FCM-based FSVM model for more accurate predictions.

The purpose of this paper is to design a modified version of the double sampling plan to handle the inspection processes requiring a minimum sample size to assure the median life for the products under the new Weibull–Pareto distribution. The economic design of the proposed plan is also considered to assure the product's lifetime with minimum cost.

The authors have developed an optimization model for obtaining the required plan parameters by solving simultaneously two non-linear inequalities and such inequalities have been formed based on the two points on the operating characteristic curve approach.

The results show that the average sample number, average total inspection and total inspection cost under the proposed plan are smaller than the same of a single sampling plan. This means that the proposed plan will be more efficient than a single sampling plan in reducing inspection effort and cost while providing the desired protection.

The proposed modified double sampling plan designed to assure the median life of the products under the new Weibull–Pareto distribution is not available in the literature. The proposed plan will be very useful in assuring the product median lifetime with minimum sample size as well as minimum cost in all the manufacturing industries.

To evaluate the inhibition efficiency (IE) of polyvinyl alcohol (PVA) in controlling the corrosion of carbon steel immersed in neutral aqueous solutions containing 60 ppm of Cl⁻, in the absence and presence of Zn²⁺. To investigate the influence of sodium sulphite (Na₂SO₃), sodium dodecyl sulphate (SDS), pH and duration of immersion on the IE of PVA‐Zn²⁺ system. To analyse the protective film formed on the metal surface.

The IE has been evaluated by weight loss method. The protective film was analysed by FTIR and fluorescence spectra.

A formulation consisting of 100 ppm of PVA and 75 ppm of Zn²⁺ offered 81 per cent IE to carbon steel immersed in a solution containing 60 ppm of Cl⁻. A synergistic effect on inhibition of a combination of PVA and Zn²⁺ was observed during the tests. The protective film consisted of the Fe²⁺‐PVA complex and Zn(OH)₂. It was found to be UV‐fluorescent. When SDS was added to the PVA‐Zn²⁺ system, the mixture showed maximum IE at the critical micelle concentration (200 ppm) of SDS (an anionic surfactant). The oxygen‐scavenging effect of Na₂SO₃ increased as the concentration of Na₂SO₃ was increased. At lower concentrations of Na₂SO₃, the transport of the inhibitors played a more major role than did the removal of dissolved oxygen. As the pH value was increased, the IE of the PVA‐Zn²⁺ system decreased. As the duration of immersion was increased, the IE was observed to decrease.

Electrochemical studies such as polarization and AC impedance spectra will enlighten more on the mechanistic aspects of corrosion inhibition.

If this study is carried out at high temperature under simulated conditions, the findings may find applications in cooling water systems.

The role of transport of inhibitors towards the metal surface from the bulk of the solution, formation of micelles by surfactants, removal of dissolved oxygen by oxygen scavenger, competition between formation of insoluble iron‐inhibitor complex on metal surface and formation of soluble iron chloride in influencing the inhibitive property has been investigated. The protective film was analysed by FTIR spectra and fluorescence spectra.

With over-the-top (OTT) streaming services rapidly transforming the media industry and saturating the market, the authors' study seeks to enrich the goal-directed behaviour model by exploring how perceived risks and descriptive norms influence OTT consumption.

Survey data from OTT subscribers were collected online to assess their risk behaviours. The 353 responses obtained were analysed with SmartPLS, validating the structural equation modelling (SEM) through structural and measurement model verification.

The authors' findings illustrate that descriptive norm, perceived behavioural control, as well as positive and negative anticipated emotion (NEM) and attitude, contribute positively to the desire to engage with OTT streaming services. Interestingly, the authors' study contradicts common assumptions, revealing that subjective norms do not significantly impact the propensity to utilise OTT services. This counterintuitive finding necessitates a reconsideration of prevalent theories and contributes to a nuanced understanding of OTT adoption determinants.

The data gathering for this study were conducted from the perspective of a single nation. Therefore, caution must be exercised when generalising this study's results.

The practical ramifications of this research are vast, providing OTT service providers and marketers with actionable insights to maximise user engagement and navigate perceived risks related to OTT service adoption and consumption.

This study's exploration of perceived risks and descriptive norms enhances the goal-directed behaviour model's breadth, facilitating a holistic comprehension of the constructs shaping OTT consumption behaviours. It would be the first attempt to combine perceptual, affective and behavioural factors and perceived risks to understand the user's predisposition to engage in OTT streaming services.

This study aims to examine values derived from apps and their relationship with continual intention using reviews from the Google Play Store.

This paper delves deep into the determinants of mobile health apps’ (MHAs) value offering (functional, social, epistemic, conditional and hedonic value) using automatic content analysis and text mining of user reviews. This paper obtained data from a sample of 45,019 MHA users who have posted reviews on the Google Play Store. This paper analyzed the data using text mining, ACA and regression techniques.

The findings show that values moderate the relationship between review length and ratings. This paper found that the higher the length, the lower the ratings and vice versa. This paper also demonstrated that the novelty and perceived reliability of the app are the two most essential constructs that drive user ratings of MHAs.

This is one of the first studies, to the best of the authors’ knowledge, that derives values (functional, social, epistemic, conditional and hedonic value) using text mining and explores the relationship with user ratings.

This chapter draws from bibliometric data and secondary sources to explore the interrelation between sustainable development and organic agricultural practices. The study contends that the adoption of organic farming methods holds the potential to address multiple Sustainable Development Goals (SDGs), thereby contributing to the well-being of all living organisms. The analysis of current trends in organic agriculture research has revealed prevalent themes pursued by academics. Notably, themes such as biotechnology, biodegradation, soil conversion, soil restoration, environmental sustainability, health, alternative agriculture and community development emerge as significant and burgeoning areas of study within the field. Additionally, other themes have come to the forefront in the findings, including agricultural emissions, cultivation practices, environmental regulations, erosion control, agricultural policy and sustainable agriculture. These themes present vital areas for further investigation and exploration, indicating areas where more research is warranted. This qualitative piece through content analysis provides a deep insight on the fact that organic farming in relation to sustainability is a very under researched area. Researchers and practitioners can explore this area as a tool to achieve the goals of sustainability in diverse dimensions. The study suggests some under-researched areas as future research agendas which include supply chain and distribution management, market development and consumer behaviour in relation to organic farming, investigating and creating novel commercial strategies for organic farming, financial and investment mechanisms in the field of organic farming, technological developments in organic agriculture, analysing rules and regulations to see how they affect the development and sustainability of organic farming and social impact and stakeholder engagement for organic farming as tool to promote sustainability.

The exponential growth of the construction sector exerts considerable pressure on the environment, and the circular economy (CE) concept has recently gained traction as a means of alleviating such environmental issues. In order to propose solutions to the phenomenon of contractors being hesitant to participate in CE implementation in developing countries, this paper aims to comprehensively explore the enabling factors that motivate contractors to implement CE.

This study is based on the push-pull-mooring (PPM) theory and extends it by introducing information provision (IP) as a trigger on contractors’ intrinsic subjective initiative states on CE implementation. The study considers what and how positive pull, negative push and neutral mooring factors influence the contractors’ CE transition. The framework was validated by questionnaires collected from contractors in China, and the data were analyzed using the structural equation modeling method.

The findings indicate that all factors from the PPM framework exert a positive influence on CE intention, with push factors demonstrating a greater average impact. Furthermore, this study confirms the influence of the IP on contractors’ CE intentions by influencing their intrinsic status. The impact of the IP is most pronounced in terms of contractors’ perceived usefulness and environmental concerns.

This study contributes to the existing body of knowledge in the CE transition studies by extending the PPM theory into the construction industry through the lenses of contractors in developing countries. The results highlight the trigger impacts of the IP on contractors’ psychological status regarding CE transition. Furthermore, it offers insights into government management in the CE transition by providing the government with novel approaches to facilitate the CE transition in the construction sector.