The asymmetric vehicle routing problem with simultaneous pickup and deliveries is considered. This paper develops four new classes of valid inequalities for the problem. We generalize the idea of a no-good cut. Together, these help us solve 45-node randomly generated problem instances more efficiently. We report results on a set of benchmark instances in literature. In this set, we are able to show an order of magnitude improvement in computational times over currently published results in literature. © 2019 Elsevier B.V.

Due to high consumption of energy, its associated concerns such as energy security and demand, wastage of resources, and material-energy recovery are leading to the importance of sustainable energy system development. This is a high time to assess the sustainability in energy systems for meeting the requirements of energy with an enhanced economic, ecological, and social performance from a nation context. The energy system plays a significant role in deciding the economic progress of emerging economies such as India, China, Brazil, and Africa. In this paper, an original attempt has been made to list and evaluate important indicators for sustainability assessment of energy systems development and management in an emerging economy especially India. Firstly, based on the analysis of the extant literature and then followed by expert opinion, potential key sustainability assessment indicators for energy systems development and management were identified. Further, grey based Decision-Making Trial and Evaluation Laboratory technique to understand the causal interactions amongst indicators and segregate them into cause and effect groups, is used. This work can provide useful aids to decision making bodies, sustainability practitioners and business organisations in selective implementation, monitoring and control of sustainable strategies in energy systems development and management and meeting sustainable development goals of clean energy in a nation context. © 2019

Information Technology (IT) infrastructure has been integral to operational excellence approaches towards sustainable supply chains. The paper explores the crucial barriers to IT implementation in the largely unorganized logistics sector of an emerging economy, India. Through a comparative study using two separate expert-groups, representing the organized and unorganized logistics sectors respectively, the paper analyses their views on the criticality of barriers in each sector and highlights the sector-specific problems that hinder effective IT implementation. Considering the profiles of the expert groups used, the paper implements Fuzzy-ISM and Fuzzy-MICMAC analysis for one group and IRP for the other to capture and analyze their opinions appropriately. The results obtained from the analyses show that the barriers concerning IT implementation in the unorganized logistics sector (ULS) substantially differ from those concerning the organized logistics sector (OLS) as perceived by the corresponding sector experts. The study ranks the barriers in the two sectors and highlights the focus areas that require immediate attention to strengthen the IT infrastructure of Indian ULS, which suffers mainly from behavioral issues at the organizational level. Focused attention to unorganized sector's requirements can revive the sector and smoothen its transformation to an organized one as exceedingly demanded by the recent e-commerce boom. The work is perhaps the first to address the Indian ULS. It implements a unique approach: seeks inputs from two different expert groups, implements tools appropriate to the respective panel profiles, and compares their opinions to identify the specific and different requirements of the Indian ULS with respect to IT implementation. © 2019 Elsevier B.V.

We propose a novel deep learning hybrid recommender system to address the gaps in Collaborative Filtering systems and achieve the state-of-the-art predictive accuracy using deep learning. While collaborative filtering systems are popular with many state-of-the-art achievements in recommender systems, they suffer from the cold start problem, when there is no history about the users and items. Further, the latent factors learned by these methods are linear in nature. To address these gaps, we describe a novel hybrid recommender system using deep learning. The solution uses embeddings for representing users and items to learn non-linear latent factors. The solution alleviates the cold start problem by integrating side information about users and items into a very deep neural network. The proposed solution uses a decreasing learning rate in conjunction with increasing weight decay, the values cyclically varied across epochs to further improve accuracy. The proposed solution is benchmarked against existing methods on both predictive accuracy and running time. Predictive Accuracy is measured by Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R-squared. Running time is measured by the mean and standard deviation across seven runs. Comprehensive experiments are conducted on several datasets such as the MovieLens 100 K, FilmTrust, Book-Crossing and MovieLens 1 M. The results show that the proposed technique outperforms existing methods in both non-cold start and cold start cases. The proposed solution framework is generic from the outperformance on four different datasets and can be leveraged for other ratings prediction datasets in recommender systems. © 2019

This research analyses the interactions and impacts of forest carbon projects on existing governance mechanisms viz. policies, institutions and implementation structures. It critically evaluates the assumption that forest carbon projects always result in improved governance of resources. Empirical evidence from a World Bank funded project in the state of Himachal Pradesh in India suggests that these projects might conflict with existing policies and complicate property relations on the ground. These projects might be used by powerful stakeholders as a tool to scuttle the progressive policy reforms. In the case of Himachal Pradesh, state bureaucracy and local elites have used forest carbon project to thwart the progressive policy provisions of HP common land vesting and utilisation (Amendment) Act 2001 and Forest Rights Act 2006, which were enacted to recognise individual and community rights on the community and forest lands respectively. Similarly, existing institutional issues related to lack of inclusiveness, participation and transparency have been exacerbated by the availability of increased resources from the project. Project activities such as fencing and changing land use of community pastures through plantations can adversely impact local livelihoods. Hence, it is imperative to undertake a comprehensive assessment of potential risks and conflicts before implementation of such projects. © 2019

Across the globe, the awareness for environmental degradation and its harmful effects is rapidly growing. The whole world has come together to work in the direction to protect the environment. Consumers are increasingly becoming cautious towards the impact of their consumption pattern on environment and organisations can attain a competitive edge by leveraging this cautiousness by offering them green products/brands. However, it is importance for the marketers to understand that how increasing levels of sustainability awareness impacts other factors which explain pro-environmental behaviour of customers. To fill the existing gap in the current literature in this regard, the current study aims to build a structural model which includes social and environmental sustainability awareness in measuring customer altruism, buying intention, loyalty and customer evangelism. The theoretical model extends the existing framework of the Theory of Planned Behaviour (TPB) and explores the decision-making framework regarding ethical behaviour. Through existing literature review and expert input, the indicators (variables) for each construct were recognised. After that, data was collected from 331 respondents through a structurally designed questionnaire; the hypothetical model was test using the Structural Equation Modelling (SEM) technique. The findings of the study indicate that sustainability awareness positively influence the consumer altruism which in turn enhances the consumer purchase intention, green brand loyalty and green brand evangelism and altruism can and can bridge value-action gap for green brands. Current analysis supports the view that there are significant positive associations among the identified constructs. © 2019 Elsevier Ltd

The article presents a unique approach to explore and evaluate the critical factors of supply chain management-marketing (SCM-M) interfaces in e-commerce. This article systematically identifies the critical factors of SCM-M interface in e-commerce using DeLone and McLean (D&M) Model of IS Success. The critical factors identified are used to form the dynamic scenarios based on Fuzzy Cognitive Maps (FCM) for strategizing the e-commerce success. To understand and evaluate the proposed research methodology, some scenarios were developed based on the critical factors of SCM-M interface in e-commerce. The analysis of these scenarios aid in identifying the improvement areas as well as strategizing and improving those areas of SCM-M interface in e-commerce for enhanced customer satisfaction and leveraging organizational success. © 2020, IGI Global.

Purpose: The purpose of this paper is to consider a nonlinear problem of minimizing the cost of providing reliable systems. The authors assume that the system consists of several components in series, and for each such component, the cost of the component increases exponentially with its reliability. Design/methodology/approach: In order to solve this nonlinear optimization problem, the authors propose two approaches. The first approach is based on the concept of adjusting the reliability of a pair of components to minimize the cost of the system. The authors call this procedure as reliability adjustment routine (RAR). Proofs of optimality and convergence for the proposed model are also provided. The second approach solves the problem by using a Lagrangian multiplier. A procedure is developed to obtain the maximum step size to achieve the desired optimal solution in minimum iterations. Proposed approaches are efficient and give exact solutions. Findings: Proposed methods enable a decision maker to allocate reliability to the components in series while minimizing the total cost of the system. The developed procedures are illustrated using a numerical example. Although an exponential relationship between the component cost and reliability is assumed, this can be extended to various other nonlinear distributions. Originality/value: This cost optimization problem, subject to system component reliability values, assumes the near practical nonlinear pattern of cost vs reliability. Such problems are complex to solve. The authors provide a unique approach called RAR to solve such convoluted problems. The authors also provide an approach to solve such problems by using a Lagrangian multiplier method. Various proofs have been worked out to substantiate the work. © 2019, Emerald Publishing Limited.

With rising concerns about the steep increase in air pollution in the National Capital Territory of Delhi, several factors-particularly motorised transportation, construction, and stubble burning in neighbouring states-are being identified as contributing to this hazard. However, in order to make effective policy decisions, there is a need for a holistic approach that identifies the root causes of the problem. The use of system dynamics simulation offers a novel systems thinking approach to understand Delhi's air pollution, taking into account the dynamic nature of the air pollution system as well as the complex interdependencies among the various factors and sources of air pollution. © 2019 Economic and Political Weekly. All rights reserved.

Recent studies in macro accounting suggest that changes in aggregate accounting earnings can predict quarterly GDP growth in the USA. Our objective in this study is to test the robustness of the association between aggregate earnings and real GDP growth across multiple countries and for different definitions of aggregate earnings. We test whether aggregate earnings changes predict future economic growth in eight countries–Australia, Canada, China, India, Japan, South Korea, the UK, and the USA. We find positive evidence regarding the generalisability of the aggregate earnings–real GDP relation in an international context. In additional tests, we find that economic forecasters appear to underreact to aggregate earnings information. Our results show that aggregate earnings lead economic growth and forecasts of real GDP growth can be improved by incorporating aggregate earnings information. Further, we also test if negative earnings changes contain more information than positive earnings changes and find only modest evidence in favour of this hypothesis. The results are robust to alternative statistical methodology and the removal of the USA data from the sample. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

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.

The growth of networks is prevalent in almost every field due to the digital transformation of consumers, business and society at large. The unfolding of community structure in such real-world complex networks is crucial since it aids in gaining strategic insights leading to informed decisions. Moreover, the co-occurrence of disjoint, overlapping and nested community patterns in such networks demands methodologically rigorous community detection algorithms so as to foster cumulative tradition in data and knowledge engineering. In this paper, we introduce an algorithm for overlapping community detection based on granular information of links and concepts of rough set theory. First, neighborhood links around each pair of nodes are utilized to form initial link subsets. Subsequently, constrained linkage upper approximation of the link subsets is computed iteratively until convergence. The upper approximation subsets obtained during each iteration are constrained and merged using the notion of mutual link reciprocity. The experimental results on ten real-world networks and comparative evaluation with state-of-the-art community detection algorithms demonstrate the effectiveness of the proposed algorithm. © 2019 Elsevier B.V.

Firms utilise both internal and external knowledge reservoirs in order to innovate. This study explores the drivers of innovation specifically, role of business groups, alliances, degree of internationalisation and financial slack on innovation. Hypotheses are tested using patent data on a sample of Indian firms. Group affiliation, financial slack and degree of internationalisation are found to positively impact innovation output. This study contributes towards the nascent literature on innovation in the Indian context. © 2019 World Scientific Publishing Europe Ltd.

This paper studies, whether the responses of the purchasing power of the U.S. dollar asymmetric to crude oil price fluctuations? A multiple threshold nonlinear autoregressive distributed lag model (MTNARDL) is developed to address this question. Working on the monthly data from January 1990 to June 2019, this study shows a short-run asymmetric transmission of oil price fluctuations into the purchasing power of the U.S. dollar. The results of the MTNARDL model apprehend, more minutely, the magnitude of variations in purchasing power in response to minor to major change in the oil price. It finds corroborative evidence for more rapid response of the purchasing power to the upward oil price shocks than to the downward movement in the oil price. This implies that the purchasing power of the U.S. dollar experiences sharp reduction with the rise in the oil price, nonetheless, the advantage of a drop in the oil price is not entirely transmitted to the purchasing power as its correction takes much longer period than expected. © 2019 Elsevier Ltd

This paper examines barriers to the implementation of Industry 4.0 technologies in the manufacturing sector in the context of both developed and developing economies. A comprehensive literature review, followed by discussions with industry experts, identifies 15 barriers, which are analyzed by means of a Grey Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. The ‘lack of a digital strategy alongside resource scarcity’ emerges as the most prominent barrier in both developed and developing economies. The influencing barriers identified suggest that improvements in standards and government regulation could facilitate the adoption of Industry 4.0 technologies in developing country case, whereas technological infrastructure is needed to promote the adoption of these technologies in developed country case. This study is one of the first to examine the implementation of Industry 4.0 in both developing and developed economies. This article highlights the difficulties in the diffusion of technological innovation resulting from a lack of coordinated national policies on Industry 4.0 in developing countries, which may prevent firms from fully experiencing the Industry 4.0 revolution. The results of this study may help decision makers and practitioners to address the barriers highlighted, paving the way for successful implementation of Industry 4.0 across the manufacturing sector. © 2019 Elsevier B.V.

[No abstract available]

While there has been extensive research and practitioner interest in regulated industries, the classification of regulated industries, as used in many previous studies, is dated, not comprehensive and lacks consensus. This paper provides an alternative classification of regulated industries in India based on several parameters of regulation. The usefulness of the classification index is demonstrated by showing how regulated industries differ from non-regulated industries in terms of political connections and industry concentration. © 2019

Purpose: The purpose of the study is to understand the evolution of the state-of-the-art of corporate social responsibility (CSR) research in the domain of marketing. Design/methodology/approach: The top-ranked journals in the domain of marketing have been chosen for the purpose of this study, and the papers related to CSR concept published in those journals between 1930 and 2018 have been reviewed. A lucid framework has been used to structure the reviewing process. Findings: The study finds that the CSR concept in marketing context has become more complex, its dimensions have evolved and increased in number, affecting diverse stakeholders leading to different outcomes. Research limitations/implications: A simple model is proposed to understand the amalgamation of CSR concept in marketing literature. The study also highlights extant gaps in the literature and suggests the directions for future research. Practical implications: The notion of CSR goes beyond the economic and regulatory obligation of the firm. Hence, for it to be duly implemented, practitioners must have a holistic understanding of this multi-faceted construct. This paper examines the changing role of CSR in the context of marketing over a period of almost a century. It thereby helps marketers to understand and visualize their changing responsibility toward the society at large and thereby co-create a sustainable relationship with all the stakeholders. The study provides both tactical and strategic pointers to practitioners. Social implications: The study draws upon extant literature and documents the positive impact of CSR on marketing variables and thereby gives a compelling reason to the marketers to be socially responsible. Compilation of persuasive evidence would encourage the adoption of CSR concepts by the marketers. This would elicit a more socially responsible action which will have a positive impact on the society that the marketer serves. Originality/value: This is an in-depth study depicting the journey of CSR construct in marketing literature and provides a holistic understanding of the construct in the context of marketing. © 2019, Emerald Publishing Limited.

This study elucidates plausible correlation between crude oil and agricultural commodities. We assess whether the conditional correlation of crude oil with energy crops (e.g., corn and soybeans) is different from that of food crops (e.g., oats and wheat). We find a stronger correlation of about 20 percent between returns of crude oil and energy crops. However, the correlation coefficient value for oil-oats and oil-wheat is as low as eight percent. We add to the literature by exploring correlation in a dynamic context using three different GARCH models and found that conditional correlation between crude oil and energy corps is relatively high. In order to reduce risk associated with crude oil price fluctuations, this study also examined hedging possibilities against crude oil by investment in agricultural commodities. Although hedging effectiveness is low with all underlying agricultural commodities, soybeans provide relatively better hedging possibilities compared to other agricultural crops. © 2019 Elsevier B.V.

This paper assesses the impact of corruption control and regulation quality on growth across countries over the period 1996 through 2015. After dealing with the possible endogeneity problem through the dynamic panel data models, our findings are suggestive of the positive effects of corruption control. Thus, our analysis tends to support the ‘sand the wheels' view at the aggregate level as well as for lower and lower–middle-income countries. Similar results are also obtained for regulation quality. However, contrary to the theoretical arguments, the joint effect of regulation and corruption does not seem to be significant empirically for countries from any of the income groups. Evidence is also indicative of a positive effect on trade across countries from all income groups. Although our results failed to support the natural resource curse hypothesis, countries with efficient institutions and low level of corruption are not seen to experience any resource curse. Finally, policy implications of these findings are brought out. © 2019 John Wiley & Sons, Ltd. © 2019 John Wiley & Sons, Ltd.