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The learning outcomes of this paper are as follows: to identify the pros and cons of waged employment and entrepreneurship, to identify the contextual factors influencing entrepreneurship, to set up a new venture, namely, steps, challenges involved and decision-making process, to scale up a small business; when, how and where? And to tradeoff required for scaling up a small business.

Pooja, a young management graduate from Varanasi, decided to overcome all challenges and barriers faced by a women entrepreneur and chase her lifelong dream of creating her own event management startup. After having achieved phenomenal success in a short period of time within the city, she began to receive interest from neighbouring cities as well. The decision to scale up operations was particularly difficult for Pooja, as she had funded the venture through her personal funds and personally nurtured the business and her team based on the values of quality and creativity. Like any small business, she had to decide what level of trade-off was required between scaling and dilution of control over the operations.

The case study is applicable for students of management. The learnings from the case can be applied by an individual who is looking to start a business or expand one.

Teaching Notes are available for educators only.

CSS 3: Entrepreneurship.

The study aims to analyze the impact of market participation on small paddy farmers' income and consumption expenditure. The study also estimates various determinants affecting the market participation of smallholders. Further, the study computes the efficiency of different paddy marketing channels and identifies the determinants that impact the marketing channel selection of paddy growers in Eastern India.

The study used the propensity score matching (PSM) approach to measure the impact of market participation on farm income and per capita consumption. Further, the study employed Acharya and Aggarwal's composite index approach to estimate the marketing efficiency of various paddy marketing channels. Further, a multinomial logit model was used to determine the marketing channel selection constraints.

The outcomes indicate that market participation positively impacts farm income and consumption expenditure. Education, membership in farmers' organizations, price information and distance to the marketplace significantly affect farmers' market participation. The results show that the producer–retailer marketing channel is the most efficient compared to others. However, most paddy farmers sell paddy to farmgate collectors due to a lack of market information, vehicle ownership, storage system, and inability to take the risk of venturing out of the farmgate into markets.

The study uses primary data and captures only farmers' perspectives to measure the impact of market participation, marketing channel efficiency and determinants for market channel selection. The other stakeholder's perceptions can be included in future studies.

Rarely does any study identifies the efficiency of different marketing channels for paddy farmers in India and includes cognitive factors like risk perception and trust in buyers as constraints for market channel selection.

Data envelopment analysis (DEA) has wide applications in the agricultural sector to evaluate the efficiency with crisp input and output data. However, in agricultural production, impreciseness and uncertainty in data are common. As a result, the data obtained from farmers vary. This impreciseness in crisp data can be represented in fuzzy sets. This paper aims to employ a combination of fuzzy data envelopment analysis (FDEA) approach to yield crisp DEA efficiency values by converting the fuzzy DEA model into a linear programming problem and machine learning algorithms for better evaluation and prediction of the variables affecting the farm efficiency.

DEA applications are focused on the use of a common two-step approach to find crucial factors that affect efficiency. It is important to identify impactful variables for minimizing production adversities. In this study, first, FDEA was applied for efficiency estimation and ranking of the paddy growers. Second, the support vector machine (SVM) and random forest (RF) were used for identifying the key leading factors in efficiency prediction.

The proposed research was conducted with 450 paddy growers. In comparison to the general DEA approach, the FDEA model evaluates fuzzy DEA efficiency giving the user the flexibility to measure the performance at different possibility levels.

The use of machine learning applications introduces advanced strategies and important factors influencing agricultural production, which may help future research in farms' performance.

The study assesses the relationship between institutional credit access and farmer satisfaction using contextual mediating and moderating variables. This study identifies various socioeconomic, service features and service quality determinants impacting institutional credit access.

The authors used the stratified random sampling method and selected 512 farmers from 40 villages in Maharashtra, India. Initially, the study employed probit regression analysis to identify the credit adoption determinants. Subsequently, the relationship between institutional credit and farmer satisfaction is identified through moderated-mediation analysis using the Statistical Package for the Social Sciences and Analysis of a Moment Structures (SPSS - AMOS model).

Probit model's results suggest that socioeconomic variables like education and bank distance; service quality variables like prompt service and employee behavior; and service characteristics variables like the interest rate, loan sanction time, repayment period, and documents for loan application significantly affect institutional credit adoption across the smallholders. Subsequently, the results of the moderating-mediation analysis show that working capital, perceived value and risk perception partially mediate the association between credit adoption and farmer satisfaction. The mediated effects are further moderated by farm advisory services and financial knowledge and skills.

The study is restricted in opportunity due to primary data, and it considers only farmers' perspectives to measure service quality and service features as constraints for institutional credit access.

The government, nongovernment organizations, civil societies and private institutions should provide sufficient financial knowledge and training to the farmers via extension services to utilize the borrowed capital effectively to bring economic welfare and mental satisfaction.

The existing literature rarely considered banking service quality and service features (demand side) variables as determinants of credit access. Further, the study brings novelty in examining how the capital management cognitive factors of the formal credit adopters influence the relationship between credit access and satisfaction.

This study aims to present the experimental and computational performance analysis in compact plate heat exchanger (PHE) using graphene oxide nanofluids at different concentrations and flow rate.

Field emission scanning electron microscope and X-ray diffraction were used to characterize graphene oxide nanoparticles. The nanofluid samples were prepared by varying volume concentration. Zeta potential test was done to check stability of samples. The thermophysical properties of samples have been experimentally measured. The experimental setup of PHE with 60° chevron angle has also been developed. The numerical analysis is done using computational fluid dynamics (CFD) model having similar geometry as of the actual plate. Distilled water at fixed temperature and flow rate is used in hot side tank. Nanofluid at fixed temperature with varying concentration and flow rate is used in cold side tank as coolant.

The numerical and experimental results were compared and found that both results were in good agreement. The results showed ∼13% improvement in thermal conductivity, ∼14% heat transfer rate (HTR), ∼9% in effectiveness and ∼10% in overall heat transfer coefficient at cost of pressure drop and pumping power using nanofluid. Exergy loss also decreased using nanofluid at optimum concentration of 1 Vol.%.

The CFD model can be significant to analyze temperature, pressure and flow distribution in heat exchanger which is impossible otherwise. This study gives ease to predict PHE performance with high accuracy without performing the experiment.

Resistance spot welding (RSW) is an essential process in the automobile sector to join the components. The steel is the principal material utilized in car generation because of its high obstruction against erosion, toughness, ease of support and its recuperation potential. Due to this, it was planned to study the mechanical properties, hardness and microstructure characteristics of RSW of Stainless steel 304.

In the present research, RSW of 304 stainless steel plates with 1 mm thickness and effect of current intensity, welding time, electrode pressure and holding time on nugget diameter, tensile strength microhardness and microstructure of the joints was investigated. The specimens were prepared according to the dimensions of 30 × 100 mm with 30 mm overlaps joint through the RSW machine. The tensile test of the specimen was carried out on a universal testing machine and microhardness of specimens measured using Vickers’s hardness tester. Taguchi L16 orthogonal array was used to scrutinize the significant parameters for each output.

It has been observed that the tensile strength of the specimen is affected by the current intensity and nugget diameter, and the weld time has a significant effect on the tensile strength. Microhardness is highly influenced by electrode pressure and holding time, as the increase in both these parameters resulted in the increase of microhardness. This is due to rapid cooling, which is done by the cooling water flowing through the copper electrodes.

This study was carried out using a copper electrode with a flat face with selected parameters and response factors. The study can be useful for researchers working on optimization of welding parameters on stainless steel.

This study aims to investigate the interrelationships among the Lean manufacturing (LM) adoption barriers in Indian SMEs. This issue has its own importance as LM has become the inescapable requirement for small- and medium-scale enterprises (SMEs) because of the increased concerns about quality, cost, delivery time and rapidly growing competition in the manufacturing sector and in India it is opposed by many factors/barriers. To act for the eradication of these barriers, we need to systematically analyze them.

Based on the available literature and consultation with the experts, the authors identified 16 LM barriers for Indian SMEs. The authors analyzed the interdependencies among the barriers and prioritized them using integrated Grey-decision-making trial and evaluation laboratory (grey-DEMATEL) approach.

The findings show that limited financial resources, fear in adopting new technology, lack of top management commitment and poor leadership quality are the most critical barriers for LM diffusion in Indian SMEs.

The present research is based on the experts’ inputs, which may be subject to individual biases. In developing countries, such as India, geographical influences are also possible, which are neglected in this study.

This study provides significant insights that can help SMEs to focus on critical cause group barriers to accelerate the LM penetration.

The authors have proposed a Grey-DEMATEL-based LM barrier evaluation framework. Here, the authors analyze the interrelationships among the barriers for LM and segregate them in cause and effect groups.

E-government quality (e-GovQual) and e-government user value (e-GUV) are multidimensional concepts. While previous studies have identified apparent factors influencing e-government satisfaction (e-GovSat) and e-government adoption intention (e-GovAI), such as e-GovQual and e-GUV, but they have neglected to explain the influence of the dimensions of these two concepts. The purpose of this research is to study e-government service value chain (e-GSVC) one-GovQual dimensions, e-GUV dimensions, e-GovSat and e-GovAI.

The study employs a quantitative method to test the hypotheses and validate the proposed model. Data are collected from 378 e-government users across different parts of India comprising of different demographic characteristics. The model is analyzed using structural equation modeling.

The findings highlight the impact of the dimensions of e-GovQual (efficiency, trust, reliability and citizen support) on the dimensions of e-GUV (functional, economic, social and emotional value) as e-GUV dimensions affect e-GovSat, which in turn influences e-GovAI. The results validate the e-GSVC and also stress the partial mediating role of the dimensions of e-GUV on the relationship between the dimensions of e-GovQual and e-GovSat.

The sample size of 378 may not be a proper representation of a country like India, which has huge diversity within its vast population.

The study offers practitioners a clear picture and a useful guide to better understand the drivers of value, satisfaction and adoption in the case of e-government users.

This study is probably the first attempt toward demonstrating the process influencing e-GovSat via e-GUV dimensions originating from excellent e-GovQual dimensions to ultimately trigger e-GovAI.

In the past four decades, substantial air traffic growth has triggered enthusiasm in the aviation sector. At the same time, this growth has posed challenges to its financial and environmental sustainability commitments. A buzz has been centered on introducing and supporting aviation sustainability initiatives. These challenges have led to acknowledging the need to reduce aviation fuel consumption, a function of multiple factors. The different stakeholders having a diverse type of interplay govern the effective implementation of the factors at different decision levels (strategic, tactical and operational). Thus, the present study aims to critically examine various decision levels involved to understand opportunities and requirements related to aviation sustainability.

In this study, the best–worst method is used to quantify different decision levels’ role on various factors affecting aviation fuel consumption.

The results of this study signify that tactical-level decisions are most influential in reducing aviation fuel consumption with the highest impact (0.41) followed by operational-level decisions (0.30) and strategic-level decisions (0.29), respectively.

The results point toward the critical role of middle-level hierarchy, i.e. aircraft manufacturers, airlines and others in the aviation industry’s sustainable growth. Thus, middle-level stakeholders must be inspired and empowered to act, being at the center they link the other two levels.

This study has added to the body of knowledge by exploring the decision-making competencies needed by different aviation sector stakeholders. It also presents the possible options available in the sector and the role of stakeholders at different levels in exploiting and implementing the sustainable aviation sector changes.

This study aims to generate different three-dimensional (3D) foam models using computer tomography (CT) scan and solid continuum techniques. The generated foam models were used to study deformation mechanism and the elastic-plastic behaviour with the existing experimental foam behaviour.

CT scan model was generated by combing 2D images of foam in MIMICS software. Afterwards, it was imported in ABAQUS/CAE software. However, solid continuum model was generated in ABAQUS/CAE software by using crushable foam properties. Then, the generated foam models were sets boundary conditions for a compression test.

CT scans capture the actual morphology of foam sample which may directly an image based finite element foam model. The sectional views of both the models were used to observe deformation mechanism on compression. The real compressive behaviour of foam was visualised in CT-Scan foam model. It was observed that CT-scan model was the more accurate modelling method than crushable foam model.

The internal structure of foam is very complex and difficult to analyse. Therefore, CT-scanning may be the accurate method for capturing the macro-level detailing of foam structure. A CT-scan foam model can be used for multiple times for mechanical analysis using a simulation software, which may reduce the manufacturing and the experimental cost and time.