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This paper aims to examine the gender differences in various measures of travel behavior for a large-sized Indian city, taking into account the existing class differences.

Stratified random sampling technique is used to collect primary data for travel behavior. The collected data is then differentiated on the basis of socioeconomic characteristics and gender. Descriptive statistics are used for analysis.

The findings confirm that, women mostly walk and men use motorized vehicles. With an improvement in socioeconomic status, women switch over to public transport and men continue to ride motorized vehicles. While the number of women making everyday trips declines with a rise in socioeconomic status, the number of men rises.

The study points out at the need for development of an adequate infrastructure of nonmotorized transport and public transport in the city which attends to not only the issue of environmental quality but also of women’s empowerment.

To the best of the authors’ knowledge, this is the first time a comprehensive analysis of differences in travel behavior between men and women on the basis of socioeconomic status is carried out in this region. This analysis will facilitate the policy makers in understanding the inconsistencies in transport demand between the two groups of population.

The purpose of this paper is to understand the role of entrepreneurial intention in promoting women entrepreneurship in Indian micro, small and medium enterprises (MSMEs). This study seeks to clarify the construct of entrepreneurial intention and then reports the validation of the entrepreneurial intention instrument.

An instrument has been designed and administered on a sample of 103 respondents across India from women entrepreneurs to understand the entrepreneurial intention by using cluster and snowball sampling. The data has been streamlined and then analyzed using descriptive analysis for validity and reliability checks.

This research was aimed to determine the constructs of entrepreneurial intention. Through data analysis, it has been observed that the reliability coefficients reveal the adequacy of the sample. The Cronbach’s alpha values for all the items in the instrument were found to be greater than or equal to 0.6. Strong correlations were also found between direct and indirect measures of entrepreneurial intention and hence confirmed that all the measures in the instrument were well constructed. Analysis has also explained the relationship between various constructs of entrepreneurial intention by using Pearson’s correlation coefficients. Strong and positive values of correlation explain the existence of the convergent and discriminant validity of the instrument.

The research results obtained from the analysis of reliability and validity tests not only provides the establishment of the relationship among the various constructs but also suggests that the model provides a promising potential to measure entrepreneurial intention. This study will contribute to new knowledge of the conditions of women entrepreneurship from different perspectives by developing and validating an analytic model for promoting the women entrepreneurship in MSMEs of India.

From a government perspective, this model will help in designing training programmes for promoting women entrepreneurship in India. The obtained result also brings significant implications for practice as well as raises a broad future direction for other researchers

Extended SCCT model has recently suggested an inclusive framework of factors affecting the entrepreneurial intention, there is not much attempt made in research using this theory as background for predicting intention in the context of women entrepreneurship. This paper attempts to fill this gap by formulating a conceptual model for measuring entrepreneurial intention among women entrepreneurs by integrating and adapting the constructs of extended social cognitive career theory model and entrepreneurial potential model.

Using survey data of a developing country city, this study aims to examine the impact of different socio-demographic factors on the choice of less-polluting modes of transport for purposes other than work.

Stratified random sampling technique is employed and data on socio-demographic characteristics and mode of transport used is collected. Descriptive statistics complemented with a logit model of choice probabilities is implemented on the data obtained.

Majority of the population in the city uses motorized means of transportation irrespective of the socio-demographic changes existing among them. Women, the individuals belonging to the youngest age group, the least economically well-off group of people, the least educated and the non-working are the individuals more likely to use more of less-polluting modes and less of more-polluting modes for non-work purposes as compared to their counterparts.

The study also calls for the development of an efficient and secured system of public transportation and non-motorized transportation in the city in such a way so as to neither hamper the goal of sustainability nor the goal of empowerment.

To the best of the authors’ knowledge, this is the first time a comprehensive analysis of the influence of socio-demographic factors on choice of type of mode of transport is carried out in this region of the developing world. This analysis will facilitate the policy makers in catering to the transportation needs of different segments of the society.

Examines Just‐in‐Time (JIT) from its evolution as a Japanese concept through to a review of its philosophy and implementation. Cites several techniques of implementation. Includes a review of the early work of various researchers and practitioners. Concludes that JIT is a very effective manufacturing philosophy which is universal in nature encompassing all aspects of manufacturing. Suggests a few deficiencies in current literature.

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

Purpose: This chapter aims to analyse and highlight the current landscape of performance management (PM) systems, and the benefits of integrating modern technology such as smart analytics (SA) and artificial intelligence (AI) into PM systems. The chapter discusses the application of AI in PM tasks which successively simplify many offline PM tasks.

Methodology: To carry out this analysis, a systematic literature review was performed. The review covers literature detailing PM components as well as research concerned with the integration of SA and AI into PM systems.

Findings: This study uncovers the merits of using SA and AI in PM. SA technology provides organisations with a clear direction for improvement, rather than simply state failure in performance. AI can be used to automate redundant tasks while retaining the human element of decision-making. AI also helps reduce the time required to take action on feedback.

Significance: The findings of this research provide insights into the use of SA and AI to make PM tasks fast, scalable, and error-free.

Many a times, the information about the boundary heat flux is obtained only through inverse approach by locating the thermocouple or temperature sensor in accessible boundary. Most of the work reported in literature for the estimation of unknown parameters is based on heat conduction model. Inverse approach using conjugate heat transfer is found inadequate in literature. Therefore, the purpose of the paper is to develop a 3D conjugate heat transfer model without model reduction for the estimation of heat flux and heat transfer coefficient from the measured temperatures.

A 3 D conjugate fin heat transfer model is solved using commercial software for the known boundary conditions. Navier–Stokes equation is solved to obtain the necessary temperature distribution of the fin. Later, the complete model is replaced with neural network to expedite the computations of the forward problem. For the inverse approach, genetic algorithm (GA) and particle swarm optimization (PSO) are applied to estimate the unknown parameters. Eventually, a hybrid algorithm is proposed by combining PSO with Broyden–Fletcher–Goldfarb–Shanno (BFGS) method that outperforms GA and PSO.

The authors demonstrate that the evolutionary algorithms can be used to obtain accurate results from simulated measurements. Efficacy of the hybrid algorithm is established using real time measurements. The hybrid algorithm (PSO-BFGS) is more efficient in the estimation of unknown parameters for experimentally measured temperature data compared to GA and PSO algorithms.

Surrogate model using ANN based on computational fluid dynamics simulations and in-house steady state fin experiments to estimate the heat flux and heat transfer coefficient separately using GA, PSO and PSO-BFGS.

The purpose of this paper is to analyze significant determinants to assess the probability of insureds’ intent to buy (ITB) insurance and willingness to pay (WTP) quantum of dollars for security benefits.

The authors use the Double Hurdle Model (DHM) and Neural Network (NN) architecture to analyze the insureds’ behavior for ITB and WTP. The authors apply these frameworks to all the 503 insureds of a branch of a leading insurer in the United Arab Emirates.

The DHM identified age, loans & liabilities, body mass index, travel outside the UAE, salary and country of origin (Middle Eastern and African) as significant determinants to predict WTP for social security benefits. In addition to these determinants, NN architecture identified insurance replacement, holding multiple citizenship, age of parents, mortgages, country of origin: Americas, length of travel, income of previous year and medical conditions of insured as additional important determinants to predict WTP for social security benefits; thus, NN is found to be superior to DHM due to its lowest RMSE and AIC in the holdout sample and also its flexibility and no assumptions unlike econometric models.

Insureds’ data used from one UAE Branch limit the generalizability of empirical findings.

The study findings will enable the insurers to appropriately design the insurance products that match the insurers’ behavior of ITB and WTP for social security benefits.

The study findings have the potential for insurance institutions to be more flexible in their insurance practices through public–private partnerships.

This is the authors’ original research work.

To explore a hybrid approach in order to attain optimal cutting conditions proficient of generating adequate dimensional accuracy in combination with virtuous surface finish during turning of commercially pure titanium (CP-Ti) grade 2.

In the present paper, an application of the hybrid fuzzy–VIKOR method has been proposed to estimate an optimal combination of process variables during turning of commercially pure titanium (CP-Ti) grade 2. Three distinct input factors, namely, cutting speed, feed rate and depth of cut, were selected, each varied at three levels. Thus, a series of experiments were performed based on Taguchi's 3-factor-3-level (L₂₇) orthogonal array. The major attention was given to acquire minimum cutting force and flank wear along with good surface finish. The adequacy of the proposed methodology was verified with the help of ANOVA test.

The results of the investigation revealed that the suggested hybrid technique is quite effective, easily understandable and time-saving approach, which can be successfully implemented to solve various problems either of similar or of different kinds.

Increasing demand of qualitative as well as low cost products is identified as the main challenging task in the current competitive market. Therefore, estimation and selection of the most suitable machining environment are of paramount importance in a real-time manufacturing system. Machining process involves both qualitative and quantitative factors, may be conflicting in nature, all to be considered together. Consequently, an appropriate combination of the machining variables is evidently desirable to meet the aforesaid challenges effectively.

The main purpose of this study is to identify the determinants that inhibit the adoption or usage of digital payment systems (DPSs) in India.

This study used a qualitative technique, including in-depth semi-structured interviews. Data analysis was conducted using thematic analysis, incorporating both deductive categorisation and inductive coding to identify factors responsible for the non-adoption or discontinuation of DPS use.

The findings are in the form of themes and sub-themes that were generated from the data analysis: digital divide (DD), which includes the digital access divide, digital capability divide and digital innovativeness divide; socio-demographic divide (SD), which includes education, geographical location, gender, age and income; psychological barriers, which include a lack of perceived ease of use, vulnerability to risks, technophobia and a lack of trust; and other barriers, which include a lack of awareness, a cash-dominated society and a lack of interoperability.

The factors identified in this research can be further validated and tested in future studies using quantitative data. This will enable stakeholders to better comprehend the impacts of these factors on DPS adoption or usage.

The study’s practical implications are specifically relevant to the Union Territory (UT) administration of Ladakh, as there is a DD and an SD among different sections of the population of the UT of Ladakh. UT administrations must prioritise efforts to eliminate these divides. The implications for banks and DPS providers are that they should conduct financial literacy training about DPSs in remote rural areas and invest in developing user-friendly and simplified DPS user interfaces to improve relationships with DPS users and their long-term retention.

The findings of this study reveal the three levels of the DD that determine DPS adoption or usage, which have not been discussed together in the literature in the DPS context and that must be addressed to expand DPS adoption, thus providing a more holistic view of the DD in the context of DPS.