In this paper, a multi-objective manufacturing cell design problem is studied. A goal programming (GP) embedded real-coded genetic algorithm (GA) is designed for solving this problem. Initially, the GA is used to obtain the individual minimum of each objective. Thereafter, utilising the concepts of GP, an equivalent problem is derived, and the sum of deviation variables associated with the objectives are minimised. The GA is used further to obtain the optimal cell design. A software toolkit is developed based on the proposed technique using C Sharp.net to ensure its use in a larger scale. The effectiveness of the technique is judged based on a set of test problems of different sizes. The proposed technique is found to be better in terms of the performance measure over the existing ones. Copyright © 2019 Inderscience Enterprises Ltd.

In view of complexities associated with supplier performance evaluation based on traditional business criterions (such as costs, quality levels, and delivery timelines) and emerging criterions (such as those related to environmental sustainability), we in this research evolve two different supplier efficiency measurement models that unify such criterions possessing characteristics of both desirable and undesirable outputs. The first model is a single-objective DEA efficiency assessment model wherein both types of outputs are integrated into a single composite efficiency measure. Using data from suppliers of Hyundai Steel Company, we determine composite efficiencies of each of these suppliers thus ranking them in terms of an overall efficiency score that would be useful as far as the first-cut supplier discrimination is concerned. However, due to the relative inability of evolved single-objective efficiency model to perform trade-offs amongst desirable and undesirable outputs and, owing to unidimensionality aspects, we evolve a goal programming based bi-objective efficiency model wherein trade-offs can be performed between both conventional and emerging dimensions criterions leading to different supplier evaluations for varied scenarios. We also integrate our evolved models with the cross-efficiency view of efficiency determination in order to enable the decision-makers to achieve peer-to-peer evaluation and maximum discrimination amongst suppliers. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

The study examines panel data for 46 Indian banks with 31 bank specific financial ratios over eight years (2007 to 2014). Together, these ratios reflect operating capability, liquidity, solvency, profitability, capital adequacy and business development capacity aspects across Indian banks that affect non-performing assets (NPAs). The data was analysed using a GMM model that dealt with endogeneity issues present in the data. This model captured NPA with an r-square of 85%. We find a negative significant relationship between intermediation cost ratio, Return on Assets and NPAs. Asset growth, lagged NPAs, and total liabilities by total assets are positively related to NPAs. © 2019

This study examines the influence of Valence and Source of Online Reviews on a customer's attitude and purchase decision in the context of public and private consumption using social influence theory and the concept of negativity bias. The study was conducted using a 2 × 3 × 3 online experiment to examine the influence of review valence (positive vs mixed vs negative), two sources (retailer vs third-party site) in two different consumption contexts (public vs private). The results highlight the role of review valence as well as consumption context on a shopper's decision. © 2019 Elsevier Ltd

Purpose: The purpose of this study is to provide an extended model of the theory of planned behaviour (TPB) and examine how institutional infrastructure and two social factors – peer effect and gender discrimination – determine entrepreneurial intention in emerging economies such as India. Design/methodology/approach: Using a structural equation modelling (SEM) approach, the proposed model is tested on a sample of 265 post-graduate students from the Indian Institute of Management campuses in India. Perceived gender discrimination is used as a grouping variable to test its impact in the model. Findings: The study found that peer effect has positive association with an individual attitude towards entrepreneurship. It also found positive moderating effect of institutional infrastructure on entrepreneurial intention in a regional entrepreneurial ecosystem, but in case of entrepreneurial self-efficacy, the result is non-conclusive. Further, our results establish that gender discrimination has varying influence but remains a significant factor to choose entrepreneurial career in emerging countries such as India. Research limitations/implications: The study adds to the understanding of drivers or antecedents to entrepreneurial intention, especially in emerging economy context and finds its usefulness in country such as India. It contributes to the existing model by empirically proving the role of peer effect and institutional infrastructure in emerging economies which are characterised by weak institutions and inadequate access to resources to start and sustain any entrepreneurial activity. Practical implications: The results make several implications for academic institutions, entrepreneurial ecosystem stakeholders and policy makers in emerging economies. The academic institutions, universities and incubation hubs can consider promoting a peer-group culture that strengthens individual’s EI in emerging economies. Policy makers and stakeholders should build an encouraging entrepreneurial ecosystem with adequate institutional infrastructure to promote entrepreneurship. Originality/value: The authors extend research on entrepreneurial intention beyond individual factors (personal-level variables) and examine the contingent role of institutional infrastructure and socio-cultural factors (peer effect and gender discrimination). Whilst TPB successfully predicted entrepreneurial intention in Western settings, this study provides strong empirical evidence to this research in emerging countries. © 2019, Emerald Publishing Limited.

Purpose: Typically, the budgetary requirements for executing a supplier’s process quality improvement program are often done in unstructured ways in that quality improvement managers purely use their previous experiences and pertinent historical information. In this backdrop, the purpose of this paper is to ascertain the expected cost of carrying out suppliers’ process quality improvement programs that are driven by original equipment manufacturers (OEMs). Design/methodology/approach: Using inputs from experts who had prior experience executing suppliers’ quality improvement programs and employing the Bayesian theory, transition probabilities to various quality levels from an initial quality level are ascertained. Thereafter, the Markov chain concept enables the authors to determine steady-state probabilities. These steady-state probabilities in conjunction with quality level cost coefficients yield the expected cost of quality improvement programs. Findings: The novel method devised in this research is a key contribution of the work. Furthermore, various implications related to experts’ inputs, dynamics related to Markov chain, etc., are discussed. The method is illustrated using a real life of automotive industry in India. Originality/value: The research contributes to the extant literature in that a new method of determining the expected cost of quality improvement is proposed. Furthermore, the method would be of value to OEMs and suppliers wherein the quality levels at a given time are the function of quality levels in preceding period(s). © 2019, Emerald Publishing Limited.

This paper attempts to explore the adoption of changes introduced by an IT-intervention in the context of a social welfare scheme. Using case study methodology, we have studied the changes introduced through the project ‘CORE PDS’ (Centralized Online Real-time Electronic Public Distribution System) in the Chhattisgarh Public Distribution System (PDS). CORE PDS was a far-sighted project aimed to integrate the retailers (Fair Price Shops, FPSs) with IT infrastructure. It simultaneously introduced two changes. One was an automated transaction processing system while another was the provision of portability for the beneficiaries. After 2 years of its implementation it was observed that despite the government’s diligent efforts, only the automated transaction processing system was adopted, while portability was discarded by the FPSs. We use the theoretical lenses of stakeholder theory and agency theory for identifying reasons for this partial failure. Our analysis suggests that information system (as monitoring and reporting mechanism), outcome uncertainty, risk aversion and goal conflict play critical roles in the adoption of changes. Changes with higher monitoring, lower outcome uncertainty and lower goal conflict are easily adopted, and on the other hand, lower monitoring, greater outcome uncertainty and higher goal conflict result in lower possibility of adoption. © The Author(s) 2017.

This paper presents a novel predictive modeling framework for forecasting the future returns of financial markets. The task is very challenging as the movements of the financial markets are volatile, chaotic, and nonlinear in nature. For accomplishing this arduous task, a three-stage approach is proposed. In the first stage, fractal modeling and recurrence analysis are used, and the efficient market hypothesis is tested to comprehend the temporal behavior in order to investigate autoregressive properties. In the second stage, Granger causality tests are applied in a vector auto regression environment to explore the causal interaction structures among the indexes and identify the explanatory variables for predictive analytics. In the final stage, the maximal overlap discrete wavelet transformation is carried out to decompose the stock indexes into linear and nonlinear subcomponents. Seven machine and deep learning algorithms are then applied on the decomposed components to learn the inherent patterns and predicting future movements. For numerical testing, the daily closing prices of four major Asian emerging stock indexes, exhibiting non-stationary behavior, during the period January 2012 to January 2017 are considered. Statistical analyses are performed to ascertain the comparative performance assessment. The obtained results prove the effectiveness of the proposed framework. © 2019

Purpose: The purpose of this paper is to decipher the possibility of ensuring the effectiveness of sales training by focusing on selected organization-related variables and thereafter aims at making recommendations for enhancing the overall sales training effectiveness. Variables that are under organizations’ control, like training transfer climate, training evaluation and degree of choice (mandatory or by choice) were selected for this study. The study also attempted to understand how these variables influence at pre-training stage and post-training stage. Design/methodology/approach: This cross-sectional research involved both exploratory and conclusive phases. To test the postulated hypotheses, data were collected in two phases from sales executives of a large oil and gas petrochemical company having the presence across India. A total of 370 sales executives, who were the part of a sales training program, were approached twice, one before the training and once after the eight weeks of training. Data from the respondents who completed both pre- and post-training survey (255) were used for analyses. Findings: The results indicate that all the three predictor variables – training awareness, perceived transfer climate and the presence of an appropriate training evaluation mechanism, influence trainees’ motivation to learn. The results depict that motivation to learn mediates the association between the predictor variables and the resultant variable. Also, degree of choice was found to moderate the association between motivation to learn and sales training effectiveness. The research reinforces the significance of pre-training factors in designing sales training programs to ensure its better effectiveness. Research limitations/implications: The present study captures organizational-level variables as they are under the control of the organization and can be managed. The study included both motivation to learn and training effectiveness in the hypothesized model to understand the motivational issues in a holistic perspective. The outcomes of this research strengthen the role of trainees’ motivation to learn as a connecting variable between organizational-level variables and sales training effectiveness. Thus, the outcome of this study contributes to the literature on training motivation as well as training effectiveness. Practical implications: The findings of this study are significant for sales organizations or the organizations offering sales training aiming to enhance overall training effectiveness. Training managers must focus on these factors and ensure that all the pre-training organizational-level variables are very well-taken care of. This implies that the trainees need to be well informed about the training calendar, training policies, training content, expectations from them and how the program will be evaluated after training, along with appropriate positive transfer climate. Organizations should also include proper training evaluation mechanism to measure training at different levels. Originality/value: This study is an attempt to decipher the possibility of ensuring the effectiveness of sales training by focusing on select organization-related variables, and thereafter aims at making recommendations for enhancing the overall sales training effectiveness. The study’s uniqueness lies in simultaneous examination of influence of various variables, and that too at two different points of time (before and after the training). These two perspectives are apparently missing in the existing sales training literature. © 2019, Emerald Publishing Limited.

Purpose: Significant advances have been made in the field of healthcare service delivery across the world; however, health coverage particular for the poor and disadvantaged still remains a distant dream in developing world. In large developing countries like India, disparities in access to healthcare are pervasive. Despite recent progress in ensuring improved access to health care in past decade or so, disparities across gender, geography and socioeconomic status continue to persist. Fragmented and scattered health records and lack of integration are some of the primary causes leading to uneven healthcare service delivery. The devised framework is intended to address these challenges. The paper aims to discuss these issues. Design/methodology/approach: In view of such challenges, in this research a Big Data and blockchain anchored integrative healthcare framework is proposed focusing upon providing timely and appropriate healthcare services to every citizen of the country. The framework uses unique identification number (UID) system as formalized and implemented by the Government of India for identification of the patients, their specific case histories and so forth. Findings: The key characteristic of our proposed framework is that it provides easy access to secure, immutable and comprehensive medical records of patients across all treatment centers within the country. The model also ensures security and privacy of the medical records based upon the incorporation of biometric authentication by the patients for access of their records to healthcare providers. Originality/value: A key component of our evolved framework is the Big Data analytics-based framework that seeks to provide structured health data to concerned stakeholders in healthcare services. The model entails all pertinent stakeholders starting from patients to healthcare service providers. © 2019, Emerald Publishing Limited.

This article describes how the exponential growth of e-commerce in India and the presence of many national and multinational e-retailers has set the trend for the major overhaul of the online industry. Most of the e-retailers have failed to differentiate themselves from the competitors. This has resulted in their failure to attract and retain the right set of consumers for their respective businesses. The present paper is aimed at identifying and developing the typology of online shoppers based on importance given by them to shopping values and web portal features. The data collected was analysed using factor, cluster and correspondence analyses. The article identifies four types of online shoppers – ‘Information Seekers,’ ‘Utility Seekers,’ ‘Value Seekers’ and ‘Core Shoppers.’ Each of these four segments display significant differences and this information can be strategically used by web retailers in targeting their markets effectively. Copyright © 2019, IGI Global.

The objective of this article is to study the relationship between men, women and nature in Sarah Orne Jewett’s ‘A White Heron’ by using ecofeminist perspectives. The cultural and moral vision of Jewett is imperative to the scope of American regionalist writing and her work characterizes the extreme concern to representing the region from which the author comes. The setting of the story holds its relevance even in the twenty-first century when the world is facing a deep ecological crisis. In ‘A White Heron’, Sarah Orne Jewett narrates the story of a 9-year-old girl Sylvia, exploring the grounds around her home in search of a prized white heron. Therefore, I suggest, it is through this relationship that the author demonstrates regional sustainability through clearly defined repressive gender roles, feminizing the concept of submissiveness while masculinizing attitudes of dominance over nature and competence in dealing with the challenges that nature presents. © 2019 SAGE Publications.

We examine the effect of corruption on cash holdings and cash value using a panel data set of 4236 firms from 16 emerging market economies. We find that the cash holdings are positively related to the corruption and by managing their cash holdings upwards, the firms can benefit in the corrupt environment by trading cash. Furthermore, cash holding adds value to the firms. However, it is insignificant when the firms are operating in high corruption environment with low investor protection. Overall, the evidence suggests that corruption play an important role in shaping the cash policies of firms in emerging markets. © 2018 Elsevier B.V.

We examine the effect of corruption on firm value using a comprehensive panel data set of 4236 firms with 38,763 firm-year observations from 16 emerging market economies during 2002–2015. By using fixed-effects panel data regression models, we find a significant negative relationship between firm value and corruption. The results are also robust to subsample analyses and to the alternate measure of firm value and corruption. Overall, our study provides evidence that corruption in emerging market economies has a negative impact on firm value. Furthermore, our study contributes to the existing literature on the drivers of firm value. ©, Copyright © Taylor & Francis Group, LLC.

We examine the dependence structure between the BRICS stock and foreign exchange markets using a dependence-switching copula model. In particular, we examine dependence and tail dependence for four different market conditions, namely rising stock–appreciating currency, falling stock–depreciating currency, rising stock–depreciating currency and falling stock–appreciating currency. Our results indicate that dependence and tail dependence in the four market conditions are symmetric for all countries except Russia during negative correlation regimes. During positive correlation regimes, dependencies generally asymmetric but tail dependence is symmetric for all countries. The results further suggest the dominance of return chasing effects for India, Brazil and South Africa, and portfolio rebalancing effects for China and Russia most of the time. We further show that the co-dependencies computed using R-vine copulas are best suited to compute the portfolio VaR during the considered time period. © 2018 Elsevier Inc.

Determining price per room to be charged to customers is an important decision to be taken by hotel management. Hotels frequently change their room rates based on the demand of room, occupancy rate, seasonal pattern, and strategies undertaken by other hotels on pricing. We formulated four models to analyse how various influencing variables, such as hotel price, demand, yearly trend and monthly seasonality influence hotel revenue per available room (RevPar). To analyse a case, we used monthly accommodation statistics for Sweden taken for Swedish Agency for Economic and Regional Growth and Statistics from January 2008 to July 2017. We carried out data analysis using both multiple regression and Multivariate Adaptive Regression Splines (MARS) model and found that application of MARS can help establishing a nonlinear relationship of RevPar with other determining variables in a superior way. We also proposed the possibility of developing a better forecasting model using MARS. © 2018 Elsevier Ltd

During the colonial and the imperial era certain conceptions or images were held and cherished, largely by the white world of the non-white world. Drawing upon the postcolonial theories beginning with Said's Orientalism to Graham Huggan's ideas of postcolonial discourses being marketed and domesticated for Western consumption, an investigation is made here of travel writings about India over a period from 1977 to 2002. This article focuses on the perceptions of India in the following travel writing books: V. S. Naipaul's India: A Wounded Civilization, Mark Tully's No Full Stops in India, and Sarah MacDonald's Holy Cow: An Indian Adventure. The article discusses the different impressions that the travelers have of India and the representations that they make motivated by their contrasting personal experience and ideas about traditions and culture. For these writers India becomes a fictional construct and a sum of the tourist attraction it lays out through a chronological sequence of events. The article unravels the postcolonial agenda of the postcolonial writers and attempts to build the real image of India through distorted representations in these three travel writing books. The article reflects important cues on the travel writings in India and at the same time openly reveals their transnational link to the global sphere. © 2019 Cognizant, LLC.

This research primarily contributes to the identification of the important variables that significantly influence room pricing on the Airbnb rental platform. The study adopted a comparative approach by using three different methods—OLS, random forest, and decision tree—and applied it to a vast amount of data from the Airbnb listing dataset of 11 US cities. Each individual amenity mentioned in the listing in the textual format was used as an independent variable. We also added six other common listing variables to obtain interesting insights into the influence of these variables from the perspective of the host, guest, traveler, and tourist. Apart from identifying city-specific variable importance using different models, we estimated a composite score of variable importance that may be helpful to generalize the influence of amenities and other explanatory variables in the presence of any city-specific regional heterogeneity on the shared rental platform. © 2019 Elsevier Ltd

Purpose: We extend the literature on Public-Private Partnerships (PPPs) to understand the benefits of these incentive-compatible contracting arrangements to the private sector firms. We argue that the unique structure of PPPs helps in mitigating information asymmetry problem that drives underinvestment in the private sector of emerging markets and thereby enhances their investment efficiency. We test this hypothesis on PPPs that occurred in India over the last one decade. Methodology: We consider the investment-cash flow sensitivity. Findings: We find that, compared to the matched non-PPP investments made by the private sector firms, PPP investments result in significant reduction of investment cash flow sensitivity in the post-PPP investment period. Also, in the long run, the operating performance of the private sector firms improves in the post-PPP investment period. However, these benefits are sensitive to the nature of firm, commitment level of private sector and Government concessions. Overall, our results concur with the rising popularity of PPPs. Originality: Our paper is the first study that examines the benefits of PPP for private firms. © 2018 Elsevier B.V.

We examine the effects of international (US based) economic policy uncertainty, geopolitical risk and financial stress alike on the emerging stock markets. We consider 24 emerging markets to understand the receptivity of these markets to the various US based macroeconomic shocks. We use monthly data ranging from January 1997 to May 2018 and use the nonparametric causality-in-quantiles test as the methodological approach. We find that: (a) the impact of these shocks is heterogeneous across the markets in terms of causality and intensity. (b) the influence of EPU is mostly profound and significant as compared to other two shock indicators i.e. GPR and FS. (c) the causality-in-mean is more significant and stronger rather than the causality-in-variance. Lastly, (d) the predictability of EPU, GPR and FS is restricted in extreme upper tails. We believe these findings are relevant to the investors in EMs for the purpose of international portfolio diversification and developing investment strategies at times of turbulent economic conditions. © 2019 Elsevier Inc.