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Data science industry is a multidisciplinary field that deals with a large amount of data and derives useful information for taking routine and strategic business decisions. The purpose of this article is to examine the relationship between pricing models, engagement models, and firm performance (FP). This study also aims at uncovering the most effective pricing model and engagement model for improving FP.

Indian data scientists were the respondents of the study. A total of 213 responses were carefully chosen. The data were analyzed using structural equations on Statistical Package for Social Sciences-Analysis of Moment Structures (SPSS-AMOS) version 25 software.

The findings of the study suggested the positive and significant impact of pricing models and engagement models on FP. Value-based pricing strategies have the maximum impact on FP. On the other hand, managed services have a higher influence on FP.

By developing a multi-faceted framework, this study is a novel contribution to the field of business strategy, especially for the data science industry.

This study examines and estimates the relationship among identified factors or determinants for the adoption of water ionizers by customers.

To this end, the questionnaire is prepared to have the preferences of customers based on the factors or enablers identified for the analysis of customers' perceptions toward the adoption of water ionizers. Recipients are identified to collect their preferences for the factors responsible for the adoption. The convenience sampling method is used in administering the questionnaire for the study. The structural equation modeling (SEM) technique is used to construct the model for defining the relationship between measured and latent variables using the lavaan, haven, psych and semPlot libraries in R software.

The study reveals that product features, which include pH value, oxidation-reduction potential (ORP) and micro-clustering (MC), are influenced by environmental sustainability (ES), and as a result, product features and product outcomes (brand value (BV), customer recommendation (CR) and perceived usefulness (PU)) together influence the adoption of water ionizers by customers.

Water ionizers have appeared as consumer electronic appliances designed to alter the pH and ORP of water through an electrolysis process. There are proven benefits to improved water quality across different pHs for its health benefits (HB) and other commercial and household uses. The study recommends that potential HB perceived through water ionizers and product features, which are supported by ES, and helps customers decide on the adoption of water ionizers.

The study supports comprehending the relationships between consumer behavior, sustainable practices and innovative technologies like water ionizers as society places a greater emphasis on environmentally conscious living and sustainable living. This study aims to clarify the elements affecting the adoption and perception of water ionizers from a sustainability perspective through an extensive assessment of the literature and empirical analysis.

The purpose of this paper is to analyze various properties of anatase titanium dioxide (TiO₂) nanoparticles. Further, it proposes to implement Linear Combinations of Atomic Orbitals (LCAO) basis set under the framework of density functional theory and outline how LCAO is able to provide improved results in terms of various mechanical properties rather than plane wave and other theoretical results.

This paper provides an exploratory study on anatase TiO₂ by implementing OLCAO–DFT–LDA–LBFGS–EOS–PZ algorithms to find out various ground-level properties. The data so obtained are complemented by various analysis using mathematical expressions, description of internal processes occurred and comparison to others’ analytical results.

The paper provides some empirical insights on how mechanical properties of anatase TiO₂ improved by implementing LCAO methodology. From the analysis of electronic properties, it is seen that the anatase TiO₂ supports the inter band indirect transition from O-2p in valence region to Ti-3d in the conduction region.

Most of the electronic properties are underestimated because a single exchange-correlation potential is not continuous across the gap. This gap can be enhanced by implementing Green’s function in place of DFT and the other way is to implement self-interaction correction.

The use of anatase TiO₂ is primarily used for catalytic applications. This is also used to enhance the quality of paper in the paper industry. Additionally, this is used as a prime ingredient in cosmetic industry.

This paper fulfills an identified need to study how LCAO, another basis set, plays an important role in improving material properties.

The existing literature reflects that the connection between enablers of Industry 4.0 (I4.0), Supply Chain (SC) sustainability and reliability is understudied. To cover this gap, the purpose of this study is to identify and benchmark the enablers of I4.0 for SC sustainability to build a Reliable Supply Chain (RSC).

This study benchmarks the I4.0 enablers for SC sustainability for building a RSC and analyses them with a multi-method approach. The identified potential enablers are validated empirically. A multi-method approach of Analytical Hierarchy Process (AHP), Decision Making Trial and Evaluation Laboratory (DEMATEL) and Preference Ranking for Organization Method for Enrichment Evaluation (PROMETHEE-II) was used to investigate the influence of the identified benchmarking enablers and develop an interrelationship diagram among the identified enablers.

This study benchmarks the potential enablers of I4.0 to achieve high ecological-economic-social gains in SCs considering the Indian scenario. Digitalization of the supply chain, decentralization, smart factory technologies and data security and handling are the most prominent enablers of I4.0 for SC sustainability to build a RSC.

The findings from the study may benefit managers, practitioners, specialists, researchers and policymakers interested in I4.0 sustainability applications.

The COVID-19 pandemic situation is increasing day by day and has affected the lifestyle and economy worldwide. Due to the absence of specific treatment, the only way to control a pandemic is by stopping its spread. Early identification of affected persons is urgently in demand. Diagnostic methods applied in hospitals are time-consuming, which delay the identification of positive patients. This study aims to develop machine learning-based diagnosis model which can predict positive cases and helps in decision-making.

In this research, the authors have developed a diagnosis model to check coronavirus positivity based on an artificial neural network. The authors have trained the model with clinically assessed symptoms, patient-reported symptoms, other medical histories and exposure data of the person. The authors have explored filter-based feature selection methods such as Chi2, ANOVA F-score and Mutual Information for improving performance of a classification model. Metrics used to evaluate performance of the model are accuracy, precision, sensitivity and F1-score.

The authors got highest classification performance with model trained with features ranked according to ANOVA FS method. Highest scores for accuracy, sensitivity, precision and F1-score of predictions are 0.93, 0.99, 0.94 and 0.93, respectively. The study reveals that most relevant predictors for COVID-19 diagnosis are sob severity, cough severity, sob presence, cough presence, fatigue and number of days since symptom onset.

Treatment for COVID-19 is not available to date. The best way to control this pandemic is the isolation of positive persons. It is very much necessary to identify positive persons at an early stage. RT-PCR test used to check COVID-19 positivity is the time-consuming, expensive and laborious method. Current diagnosis methods used in hospital demand more medical resources with increasing cases of coronavirus that introduce shortage of resources. The developed model provides solution to the problem cheaper and faster decreases the immediate need for medical resources and helps in decision-making.

This paper aims to provide an overview of the application of blockchain to agri-food supply chains, including key issues and trends. It examines the state of the art and conceptual structure of the field and proposes an agenda to guide future research.

This article performs a bibliometric analysis using VOSviewer software on a sample of 205 articles from the WoS database to identify research trend topics.

The number of publications in this area has increased since 2020, which shows a growing research interest. The research hotspots are related to the integration of blockchain technology in the agri-food supply chain for traceability, coordination between all actors involved, transparency of operations and improvement of food safety. Furthermore, this is linked to sustainability and the achievement of the sustainable development gtoals (SDGs), while addressing key challenges in the implementation of blockchain-based technologies in the agri-food supply chain.

The application of blockchain in the agri-food supply chain may consider four key aspects. Firstly, the implementation of blockchain can improve the traceability of food products. Secondly, this technology supports sustainability issues and could avoid disruptions in the agri-food supply chain. Third, blockchain improves food quality and safety control throughout the supply chain. Fourthly, the findings show that regulation is needed to improve trust between stakeholders.

The paper provides a comprehensive overview of the blockchain phenomenon in the agri-food supply chain by optimising the search criteria. Moreover, it serves to bridge to future research by identifying gaps in the field.

This paper evaluates the working capital management (WCM) efficiency of the Indian manufacturing industries through data envelopment analysis (DEA) and empirically investigates the influence of several exogenous variables on the WCM efficiency.

WCM efficiency was calculated using BCC input-oriented DEA model. Further, the panel data fixed effect model was used on a sample of 1391 Indian manufacturing firms spread across nine industries, covering the period from 2008 to 2019.

Firstly, the WCM efficiency of Indian manufacturing industries has been stable over the analysis period. Secondly, the capacity to generate internal resources, size, age, productivity, gross domestic product and interest rate significantly influence WCM efficiency.

First, the selected study period has observed various economic uncertainties including demonetization and recession, so the scenario might differ in normal conditions or country-wise. Second, the findings might not be generalizable to the developed economies, since the current study sample belongs to a developing economy, which further provides scope for comparative study.

An efficient model for managing the working capital comprising most vital determinants could enhance the firms' valuation and goodwill. Also, this study would be helpful for financial executives, manufacturers, policymakers, investors, researchers and other stakeholders.

This study estimates the industry-wise WCM efficiency of the Indian manufacturing sector and suggests measures to the concerned parties on areas to focus on and provide evidence on the estimated relationships of firm-level and macroeconomic determinants with WCM efficiency.

This study develops an integrated approach combining data envelopment analysis (DEA) and structural equation modeling (SEM) for estimating the working capital management (WCM) efficiency and evaluating the effects of diverse exogenous variables on the WCM efficiency and firms' performance.

DEA is applied for deriving WCM efficiency for 212 Indian manufacturing firms over a period from 2008 to 2019. Also, the effect of human capital (HC), structural capital (SC), cost of external financing (CEF), interest coverage (IC), leverage (LEV), net fixed asset ratio (NFA), asset turnover ratio (ATR) and productivity (PRD) on the WCM efficiency and firms' performance is examined using SEM.

The average mean efficiency scores ranging from 0.623 to 0.654 highlight the firms operating at around 60% of WCM efficiency only, which is a major concern for Indian manufacturing firms. Further, IC, LEV, NFA, ATR revealed direct effect on the WCM efficiency as well as indirect effect on firms' performance, whereas CEF had only a direct effect on WCM efficiency. HC, SC and PRD had no effects on WCM efficiency and firms' performance.

The findings offer vital insights in guiding policy decisions for Indian manufacturing firms.

This study is the first to identify the endogenous nature of the relationship of HC, SC, CEF, IC altogether with firms' performance, compounded by the WCM efficiency, by applying a comprehensive methodology of DEA and SEM and provides an efficiency performance model for better decision-making.

Ghee, anhydrous milk fat, is chemically highly complex in nature. The authentication and characterization of edible fats and oils by routine chemical methods are highly laborious and time consuming. Fourier transform-mid-infrared (FT-MIR) spectroscopy has emerged as a predominant analytical tool in the study of edible fats/oils. However, sufficient attention has not been paid so far to spectral characterization of milk fat obtained from cow and buffalo milk. The purpose of this paper is to fill this void.

Ghee samples were prepared from cow and buffalo milk by the direct cream method. From each type of milk (cow and buffalo), 35 samples of ghee were prepared; thus, in total, 70 samples of ghee were obtained for the study. For assigning absorption bands in the IR spectrum, spectra of cow and buffalo ghee samples were acquired in the MIR region (4,000-650 cm⁻¹).

In FT-MIR spectra of ghee, 14 peaks were obtained at different positions and with varying intensities. They were at 3,005, 2,922, 2,853, 1,744, 1,466, 1,418, 1,377, 1,236, 1,161, 1,114, 1,098, 966, 870 and 721 cm⁻¹ for cow and buffalo ghee with almost equal intensity of absorption.

The finding of this study will be useful for characterization and authentication of ghee.

Application of IR spectral bands of ghee in the MIR region using a FT-infrared spectrometer to monitor the quality of ghee is suggested.

The purpose of this study is to explain the relationship between behavioural biases, investment satisfaction and reinvestment intention considering the effect of evolutionary psychology. The study believes that biases are not at all times bad; sometimes, biases can assist the individual investor to select the top course of action and allow them to go for the less costly mistakes, thereby helping in achieving satisficing behaviour.

Data were collected using structured and a close-ended questionnaire from a sample of 560 respondents by using multi-stage stratified sampling method. PLS-SEM was used for preliminary validation of the questionnaire. Mediation model using the structural equation model (SEM) with the help of AMOS 20 was used for the analyses. Pre-requisite assumptions for SEM were checked by using sample characteristics. The study has three constructs with multiple items; hence, the instrument validation was done by measuring the construct validity and reliability using Cronbach’s alpha, exploratory factor analysis and confirmatory factor analysis with the help of SPSS 20 and AMOS 20.

The study confirms that behavioural biases influence investment decisions in the real estate market. Further, investment satisfaction is found to have a significant and complementary partial mediating effect. The positive mediating effect of investment satisfaction between behavioural biases and reinvestment intention shows that biases are natural tendencies in response to limit to learning which can be explained by evolutionary psychology.

There are chances that the result obtained here is because of myopic decision-making behaviour in which the long-time horizon is not considered and behavioural biases, as well as evolutionary psychology, are adaptive, so the result may change in the long-time horizon, which seeks further investigations. The study talked about the relationship between behavioural biases, investment satisfaction and reinvestment intention; it will be interesting to bring some more constructs in this model, for example, investment intention and reinvestment behaviour; this can deliver a more precise picture in this regard.

Understanding such relationships will help in better clarity about the way investment is made. The study confirms that market behaviour in the real estate market is sub-optimal, which shows that there is an opportunity for attentive investors by trading and gathering on information. Real estate practitioners can get clues from market anomalies and investor phenomena; understanding these may suggest ways to use them in the market.

Reforms in the housing sector do not only satisfy one of the basic needs but also leads to holistic economic development. Besides direct contribution, it contributes to social capital.

The study extends the current knowledge base about the relationship between behavioural biases, investment satisfaction and reinvestment intention. This study investigates the behavioural biases influencing the real estate market investment decisions of non-professional investors considering the effect of evolutionary psychology.