In this article, we consider a simple step-stress model for exponentially distributed lifetime units. As failure rate is lower at the initial stress level, therefore, it is important to pay more attention to the stress changing time. Here, we consider a simple step-stress model where the stress level changes either after a prefixed time or after a prefixed number of failures, whichever occurs later. It ensures a prefixed minimum number of failures at the first stress level and also sets up a control on the expected experimental time. We have obtained the maximum likelihood estimators of the model parameters along with their exact distributions. The monotonicity properties of the maximum likelihood estimators have been established here, and it can be used to construct the exact confidence intervals of the unknown parameters. We provide approximate and bias-corrected accelerated bootstrap confidence intervals of the model parameters. We also define an optimality criteria and based on that obtain an optimal stress changing time for a given sample size. Finally, an extensive simulation study has been performed to asses the performance of the proposed methods and provide the analyses of two data sets for illustrative purpose. © 2019, Grace Scientific Publishing.

Computer simulators are widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modelling, and manufacturing. One fundamental aspect of the study of computer simulators is known as experimental design, that is, how to select the input settings where the computer simulator is run and the corresponding response is collected. Extra care should be taken in the selection process because computer simulators can be computationally expensive to run. The selection should acknowledge and achieve the goal of the analysis. This article focuses on the goal of producing more accurate prediction which is important for risk assessment and decision making. We propose two new methods of design approaches that sequentially select input settings to achieve this goal. The approaches make novel applications of simultaneous and sequential contour estimations. Numerical examples are employed to demonstrate the effectiveness of the proposed approaches. © 2019, Grace Scientific Publishing.

This study examines the dynamic relationship between precious metals and stock markets of major developed (G7) and emerging (BRICS) nations. We use a hybrid wavelet-based Dynamic Conditional Correlation (DCC) approach, which allows us to investigate the dynamic relationship between precious metals and stock markets in time and frequency domain. Our results suggest that DCC between the return series of precious metals and stock market varies with timescales in terms of dynamicity, persistence and strength of relationship. G7 and BRICS nations exhibit different dynamicity with precious metals over the study period (2000 to 2017). The dynamics between precious metals and G7 stock markets show similar patterns, which depicts a clustering behaviour, however, same is not true for BRICS nations. In contrast to existing literature, this study found that silver offers better hedging capability than other precious metals for both short and long-run. To construct a two-asset optimal portfolio of precious metal and stock index, palladium emerges as the most favourable option for both short and long-run. © 2019 Elsevier B.V.

Since the inception of Sustainable Development Goals (SDGs), the Next 11 (N11) countries are facing difficulties in attaining the SDG objectives, as maintaining the environmental quality has been a challenge for them. In this study, we have revisited the technology policies of these countries, and in doing so, we have tried to address the problem of environmental degradation, while addressing the issues of sustained economic growth, clean and affordable energy, and quality education. In this pursuit, we have designed two indices for environmental degradation and technological advancement, and then analyzed the association between them following the Environmental Kuznets Curve (EKC) hypothesis. The empirical analysis has been done by IPAT framework, and by using bootstrapped quantile regression and rolling window heterogeneous panel casualty tests, over a period of 1990–2017. Following the results obtained from the analysis, we have tried to address the objectives of SDG 13, SDG 4, SDG 8, SDG 9, SDG 7, and SDG 10. © 2019 Elsevier Ltd

This paper analyses the changing impact of oil price shocks on a bouquet of metal and agro prices and their implications for investment decisions, during different oil price regimes, separated by structural breaks. Endogenously identifying the structural breaks, we use network analysis to decipher the nature and extent of such shock transfer across different sub periods. We suggest optimal portfolios based on conditional variance estimates to hedge oil shocks during each period. This is the first study to analyse the portfolio decisions during specific oil price regimes. The results are of significant interest to investors and policy makers. © 2019 Elsevier B.V.

Calibration of hydrological time-series models is a challenging task since these models give a wide spectrum of output series and calibration procedures require significant amount of time. From a statistical standpoint, this model parameter estimation problem simplifies to finding an inverse solution of a computer model that generates pre-specified time-series output (i.e., realistic output series). In this paper, we propose a modified history matching approach for calibrating the time-series rainfall-runoff models with respect to the real data collected from the state of Georgia, USA. We present the methodology and illustrate the application of the algorithm by carrying a simulation study and the two case studies. Several goodness-of-fit statistics were calculated to assess the model performance. The results showed that the proposed history matching algorithm led to a significant improvement, of 30% and 14% (in terms of root mean squared error) and 26% and 118% (in terms of peak percent threshold statistics), for the two case-studies with Matlab-Simulink and SWAT models, respectively. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

The aim of this study is to identify an intention towards work after retirement. This article examines the multilevel sequential mediating role of job satisfaction and affective commitment in the relationship between passion for work and intention towards work. This study has used 310 respondents retired from government jobs and data are collected through a well-administered questionnaire. Smart partial least square-structural equation modelling (SmartPLS-SEM) was used to analyse data. Path analysis of the structural model revealed that passion for work has a significant relationship with job satisfaction, affective commitment and intention towards work. The findings of the analysis show that the multilevel mediator model is significant (? = 0.08, p < 0.05) at the 5 per cent level of significance. This study supports the satisfaction-commitment model and creates intention towards work after retirement. This research is unique in the sense that it relates passion for work and satisfaction-commitment model with the work intentions of the retired persons. © 2019 Management Development Institute.

Purpose: Research in entrepreneurial cognition has called for a better understanding of interactions between contextual variables and cognitive processes. Based on previous work done on organizational learning and social networks, the purpose of this paper is to propose a formal model in which information acquisition, distribution and interpretation are tested as a function of cognition-based trust, perceived expertise and tie strength between organizational members in two different corporate entrepreneurship (CE) types. Design/methodology/approach: The authors conduct a quantitative analysis based on network data in two companies located in India. Special procedures known as quadratic assignment procedure and multiple regression quadratic assignment procedure were used to run the correlations and multiple regressions, respectively. The authors complement this analysis with interviews and qualitative information to build a rich description in each of these cases. Findings: The results indicate moderate support for the model. The evidence suggests that between both types of CE types, domain redefinition requires higher levels of tie strength, trust and perceived expertise. Sustained regeneration shows moderate significant results in tie strength, and cognition-based trust. Originality/value: The authors combined insights on social network and organizational cognitive processes to analyze interactions between context and cognition. The authors were also able to compare two different companies. The authors found consistent results regarding tie strength, but the authors also found differences between both companies, which suggest that different CE types tend to require different dynamics between context and cognitive processes. © 2019, Emerald Publishing Limited.

Returns of risky assets and their time-dependent volatility are often jointly modelled by stochastic volatility models (SVMs). Over the last few decades, several SVMs have been proposed to adequately capture the features of the relationship between the return and its volatility. The earliest SVM considers a hierarchical model, with current return as a function of the current latent volatility, which is further modelled as an auto-regressive process. In an attempt to make SVM more appropriate for complex realistic market behaviour, leverage parameter was introduced, which however led to violation of martingale difference property of the risky part of return (a necessary mean-zero condition that prevents arbitrage opportunities under mild regularity conditions). Subsequently, alternative SVMs had been developed and are currently in use. In this article, we propose mean-corrections for several generalizations of SVM with leverage that capture the complex market behaviour as well as satisfy martingale difference property of returns. We also establish a few theoretical results to characterize the key desirable features of these models, and present comparison with other popular competitors. Furthermore, three real-life examples (CITI bank stock price, euro–USD rate and S&P 500 index returns) have been used to demonstrate the performance of this new class of SVMs. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

A new method of pricing, known as pay-what-you-want (PWYW) has been successfully used in many contexts in the recent past. Success stories are in restaurants, museums, music albums, software, etc. The method where the buyer selects the price, possibly a zero price, and the seller accepts it, is a bit counter intuitive. Quite surprisingly, the PWYW pricing method has been used successfully, and some businesses have adopted it for selling their goods. We study the economic rationale behind the success of the PWYW pricing method, and analyse different situations using a simple model. Results show that the PWYW pricing method can be successful when there is a large proportion of fair-minded consumers, and a lesser proportion of free-loaders. Also, though a low marginal cost is beneficial for a PWYW method, it is not necessary. Sellers can choose to use PWYW pricing with reference prices that induce, but not bind, consumers to pay an appropriate price. Also, setting a minimum threshold price alleviates the problem of free-loaders, and the PWYW pricing method can be used profitably in the long run. © 2019 Inderscience Enterprises Ltd.

Computer simulators are widely used to describe and explore complex physical processes. The simulator outputs may come in various formats: scalar, multivariate, functional, time series, spatial temporal, to name just a few. This work focuses on computer simulators with time series outputs, which we refer to as dynamic computer simulators. We consider the inverse problem for dynamic computer simulators, that is, to estimate the set of parameters/inputs of the simulator that produces the output matching a prespecified target, or the field observation, as closely as possible. The time series nature of both the field observation and the simulator outputs makes the inverse problem significantly more challenging than in the scalar valued simulator case. In spirit, we follow the popular sequential design framework of computer experiments. However, due to the time series response, a singular value decomposition based Gaussian process model is used for the surrogate model and, subsequently, a new saddle point approximation based expected improvement criterion is developed for choosing the follow-up points. We also propose a new criterion for extracting the optimal inverse problem solution from the final surrogate. Three simulated examples and a real-life two-delay blowfly model have been used to demonstrate higher accuracy of the proposed approach as compared to the popular existing techniques. © 2019 Society for Industrial and Applied Mathematics and American Statistical Association.

Purpose: Email is a rapid and cheapest medium of sharing information, whereas unsolicited email (spam) is constant trouble in the email communication. The rapid growth of the spam creates a necessity to build a reliable and robust spam classifier. This paper aims to presents a study of evolutionary classifiers (genetic algorithm [GA] and genetic programming [GP]) without/with the help of an ensemble of classifiers method. In this research, the classifiers ensemble has been developed with adaptive boosting technique. Design/methodology/approach: Text mining methods are applied for classifying spam emails and legitimate emails. Two data sets (Enron and SpamAssassin) are taken to test the concerned classifiers. Initially, pre-processing is performed to extract the features/words from email files. Informative feature subset is selected from greedy stepwise feature subset search method. With the help of informative features, a comparative study is performed initially within the evolutionary classifiers and then with other popular machine learning classifiers (Bayesian, naive Bayes and support vector machine). Findings: This study reveals the fact that evolutionary algorithms are promising in classification and prediction applications where genetic programing with adaptive boosting is turned out not only an accurate classifier but also a sensitive classifier. Results show that initially GA performs better than GP but after an ensemble of classifiers (a large number of iterations), GP overshoots GA with significantly higher accuracy. Amongst all classifiers, boosted GP turns out to be not only good regarding classification accuracy but also low false positive (FP) rates, which is considered to be the important criteria in email spam classification. Also, greedy stepwise feature search is found to be an effective method for feature selection in this application domain. Research limitations/implications: The research implication of this research consists of the reduction in cost incurred because of spam/unsolicited bulk email. Email is a fundamental necessity to share information within a number of units of the organizations to be competitive with the business rivals. In addition, it is continually a hurdle for internet service providers to provide the best emailing services to their customers. Although, the organizations and the internet service providers are continuously adopting novel spam filtering approaches to reduce the number of unwanted emails, the desired effect could not be significantly seen because of the cost of installation, customizable ability and the threat of misclassification of important emails. This research deals with all the issues and challenges faced by internet service providers and organizations. Practical implications: In this research, the proposed models have not only provided excellent performance accuracy, sensitivity with low FP rate, customizable capability but also worked on reducing the cost of spam. The same models may be used for other applications of text mining also such as sentiment analysis, blog mining, news mining or other text mining research. Originality/value: A comparison between GP and GAs has been shown with/without ensemble in spam classification application domain. © 2019, Emerald Publishing Limited.

Nonperforming assets (NPAs) are a huge challenge infront of the Indian economy. Accumulating NPA is forming a burden and obstacle for economic growth. Financial institutions are struggling with nonperforming loans, and their efficiency is getting tremendously impacted. The aims of this study are to find out the critical factors for granting mortgage loan and to develop a formula that can help financial institutions in identifying and differentiating a possible loan defaulter from a non-defaulter. The formula developed and the identification of defaulters could help in reducing NPA of financial institutions. Managers and loan approvers can use this model to grant loans to verified borrowers and can also keep an eye on their existing customers. © 2019 John Wiley & Sons, Ltd.

Despite the vast amount of research on business model innovation, little is known about what decision-makers must consider while innovating access-based business models in the context of frontier markets. To address this research gap, we develop a comprehensive framework for access-based business model innovation in frontier markets. A participative observation approach is adopted to collect the data on the case study of Microlets, a shared mobility service in Timor-Leste, for validating the framework. We successfully demonstrate the application of the proposed framework to show how access-based business model innovations can minimize issues such as accessibility and affordability, and spur economic growth by giving importance to the factors of contextual intelligence. Three different business model innovation options were compared across the differentiating factors surrounding the contextual requirements of the frontier market to validate the relevance of our framework. By examining factors such as the institutional environment, industry dynamics and infrastructural development, the proposed framework will guide decision-makers to cope up with the inherent uncertainty of frontier markets while developing access-based business models. © 2019

While there is much emphasis on authentic green practices, there is rising concern about the common practice of "greenwashing" or unsubstantiated claims about good environmental policies. Consequently, tourism businesses need to provide tangible evidences to create trust in their green claims. This paper, building on a sequential mixed method approach of data collection, proposes a potential green service encounter (GSE) framework by which hotel management can reassure customers and secure long-term patronage. Moderating role to two key contextual variables, travel purpose and environmental values of travelers, is investigated. Practical implications indicate that hotels should offer guests opportunities for enhanced involvement in GSE that help build consumer trust. The trust building process is dependent on a traveler's environmental values but independent of trip purpose. The GSE framework conceptualization included interviews with 25 green hotel guests and a panel of experts while empirical validation involved a sample of over 500 respondents.

Purpose: This paper aims to examine various factors affecting the pricing of audit services and the selection of auditors in the Indian audit market. This paper also aims to investigate the impact of financial distress conditions on the audit pricing and auditor choice decisions of a firm, particularly in the context of a developing economy. Design/methodology/approach: The sample comprises 22,644 firm-years for 1,366 Indian firms from 1990 to 2015. The authors adopt ordinary least squares regression technique to model audit fee, and logistic regression technique to model auditor choice as a function of various factors relating to firm attributes and auditor characteristics. Findings: This paper finds that auditors tend to charge an audit fee premium when they are affiliated to a Big 4 auditor, have industry specialization or jointly provide auditing and non-auditing services. Additionally, firms with larger boards, higher proportion of independent board of directors and CEO–Chairman separation are more likely to choose a Big 4-affiliated auditor. The results also suggest that financially distressed firms tend to pay significantly lower audit fees and are more likely to choose non-Big 4 auditors. Originality/value: This paper is among the few studies which investigate how financial distress impacts the audit pricing and auditor choice decisions of a firm in the context of emerging economies. The findings of this paper raises serious concerns about the credibility of the audited financial statements and corporate governance mechanisms of firms undergoing financial distress. The empirical results of this paper have strong implications for practitioners, regulators and investors. © 2019, Emerald Publishing Limited.

Automatic text summarization can be applied to extract summaries from competitor intelligence (CI) corpora that organizations create by gathering textual data from the Internet. Such a representation of CI text is easier for managers to interpret and use for making decisions. This research investigates design of an integrated system for CI analysis which comprises clustering and automatic text summarization and evaluates quality of extractive summaries generated automatically by various text-summarization techniques based on global optimization. This research is conducted using experimentation and empirical analysis of results. A survey of practicing managers is also carried out to understand the effectiveness of automatically generated summaries from CI perspective. Firstly, it shows that global optimization-based techniques generate good quality extractive summaries for CI analysis from topical clusters created by the clustering step of the integrated system. Secondly, it shows the usefulness of the generated summaries by having them evaluated by practicing managers from CI perspective. Finally, the implication of this research from the point of view of theory and practice is discussed. © 2018, Springer Fachmedien Wiesbaden GmbH, part of Springer Nature.

The purpose of this paper is to explicate the idea of egoism in the context of marketing. The idea of egoism is reviewed and contextualized into a framework for interpreting different marketer types. Marketers’ potential trade-offs with consumers and competitors are examined. Four types of marketers are explicated: extremely egoistic marketer, moderately egoistic marketer, moderately altruistic marketer, and extremely altruistic marketer. The framework offered in the paper is of relevance to marketers, media, and agencies rewarding marketing performance. The framework may help in assessing the ethical quotient in marketers’ preferences and behaviors. Studies recognizing the pertinence of egoism in marketing are scarce, and the typological framework discussed in the paper is a contribution to the marketing ethics literature. © 2017, Springer Science+Business Media Dordrecht.

We investigate the extent of short-term flexibility in firms' working capital management decisions. Contrary to the firms' perceived ability to frequently modulate their working capital allocations, we find systematic and persistence differences in working capital allocations across and within industries. Specifically, industries and firms within industries with relatively higher or lower working capital allocations remain so for a sustained period of time, often exceeding 15 years. Contrary to the past literature suggesting that such allocations are driven by firms' concerns over improving inter-temporal cash flows and sales or absorbing shocks to their capital expenditure schedules, we find that firm-specific time-invariant factors rather primarily drive them. © 2019 Elsevier Inc.

The goal of this research is to study jointly physician prescription decisions and patient drug request behaviours. We have adopted a binary logit model and a multinomial logit model to study patient drug request data with excessive zero requests and a multinomial logit model to capture physician prescription decisions. These models are further joined by a flexible non-parametric multivariate distribution for their random effects. We also adopt an analytically consistent expression for interaction effects in our non-linear and joint modelling framework. We apply our model to a unique physician panel data set from the erectile dysfunction category. Our key empirical findings include that the triggering of drug requests by direct-to-consumer advertising (DTCA) is complex with category level DTCA reducing patients’ probabilities of making drug requests and drug-specific DTCA driving drug requests for the drug advertised, patients’ characteristics may play a role in both the influence of DTCA on drug requests and the influence of patients’ requests on physicians’ prescription decisions, patients’ drug requests have a positive effect on physicians’ prescription decisions and patients can be consistent with physicians in choosing a drug based on their diagnosis levels and some unobserved factors, and there are significant correlations between physician level random effects that drive both patients’ drug requests and physicians’ prescription decisions, which validate the joint modelling approach. © 2018 Royal Statistical Society