Lean, Six Sigma and Lean Six Sigma (LSS) are the quality improvement (QI) methodologies that have proved their merits in healthcare organisations since the 1990s. The aim of this paper is to present a systematic review of literature on the implementation of these QI methodologies in paediatrics by using a three-phase systematic review process (SRP), i.e., planning, conducting and reporting. The literature search was carried out in Web of Science, Scopus, and PubMed for articles published until April 2019. The broad range of outcomes from SRP was collated into six common themes: time optimisation, motion reduction, error mitigation, dosage optimisation, efficiency enhancement, and revenue generation. It is suggested that researchers and practitioners in paediatrics need to understand these QI methodologies deeply and deploy these in a profound way to realise their true potential. This is the first systematic review to synthesise the implementation results of QI methodologies in paediatrics. Copyright © 2021 Inderscience Enterprises Ltd.

This study aims to understand the antecedents and consequences of consumer skepticism with respect to cause-related marketing (CRM). The study examines the mediating and moderating impact of attitude toward brand image and gender on the consequences of consumer skepticism. The study findings confirm the mediation and moderation effects of attitude toward brand image and gender and the relationship between skepticism and patronage intention. No empirical evidence was found for the relationship between the strategic motive to reduce consumer skepticism and CRM. The findings are relevant for both academicians and marketers. Academicians will be enriched by the knowledge of the antecedents, moderated and mediated variables, and their differential impact on consumer skepticism about CRM. The present study significantly contributes by examining numerous psychographic variables impacting consumer skepticism, thus developing an integrated model to understand how consumer skepticism impacts patronage intention. To the best of our knowledge, no such study has been conducted in the Indian context. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Each year, millions of tons of electronic waste (or e-waste) are generated worldwide, thus, fueling concerns among scholars, practitioners, policymakers, and governments about e-waste recycling and management. The past few years have witnessed a growing interest among scholars to examine the behavioral issues concerning e-waste recycling. However, most of the existing studies have focused on adopting e-waste recycling and related innovations. It is already known that ‘reasons for’ and ‘reasons against’ the adoption of any innovation are quantitatively different. The current study bridges this gap by utilizing a novel consumer behavior framework called behavioral reasoning theory (BRT) to study e-waste recycling attitudes and intentions. The study examined the relative influence of ‘reasons for’ and ‘reasons against’ in predicting attitude and intentions within the context of e-waste recycling by using a single framework. The developed model was tested using structural equation modeling with 774 Japanese consumers. The study also examined the moderating role of environmental assessment and environmental concerns in influencing the studied associations. The results suggest that ‘reasons for’ was positively associated with attitude and intentions. The consumer values shared negative associations only with ‘reasons against.’ The study findings offer interesting insights for service providers, policymakers, and governments. © 2020 The Author(s)

This study investigates the role of authenticity, self-congruity and emotional attachment in generating positive Word of Mouth (WOM) intentions in heritage tourism. The 13th-century Sun Temple of Konark (Surya Mandira), in Odisha, India, a United Nations Educational, Scientific and Cultural Organization (UNESCO)-listed World Cultural Heritage Centre was selected as the destination for the study. A survey of residents (n = 627) and tourists (n = 473) was conducted. Statistical analyses indicate that residents and tourists are found to be different from each other in terms of their emotional attachment, existential authenticity, self-congruity, and WOM intentions. This study is one of a few that focused simultaneously on both residents and tourists, even though most of the earlier works neglected the residents’ perspective as they investigated the relationship marketing and customer-based model of authenticity. © 2020 Elsevier Ltd

SMEs around the globe are often challenged by the issues related to productivity enhancement, material usage, waste management and sustainability. Material flow cost accounting (MFCA) is considered as a green productivity tool that can be utilised by SMEs to overcome these challenges. Despite the benefits, its adoption among SMEs, especially in developing countries, remains low. The purpose of this research is to examine how MFCA can be suitably implemented in an SME set-up to improve the financial and environmental performance of the enterprise. This study utilises case based research methodology to exemplify the application of MFCA tool in an Indian steel pipes and tubes manufacturing SME. As a part of MFCA analysis, material cost, system cost and energy cost at each quantity center was calculated to identify inefficiencies in the production process of the SME. Thereafter, several solutions were implemented to address these inefficiencies. The result of the MFCA implementation showed an overall annual savings of 21,028,452 INR (USD 302,350) through an investment of 495,400 INR (USD 7123). The present study reveals that the application of MFCA tool leads to higher productivity, better energy efficiency and improved environmental performance. The SME's performance was also monitored for five years to study the long-term benefits of MFCA implementation. The findings suggest that, post MFCA implementation, the return on invested capital of the SME increased by 29.37% and the material usage cost reduced by 26.58%. This research would be beneficial to managers, practitioners, and policymakers for effectively implementing MFCA in SMEs. © 2020 Elsevier Ltd

Conventional synthetic refrigerants are responsible for ozone depletion and have high global warming potential (GWP). Thus, countries across the globe are progressively phasing out such refrigerants by 2050 under the Kigali amendment. While the developed countries in Europe are the pioneers in promoting the use of low GWP refrigerants, the developing countries are still lagging. This is happening despite these countries being a signatory to the Kigali amendment. However, it is contextual to mention here that the refrigeration industry in developing countries, such as India, faces several barriers to the adoption of low GWP refrigerants. The phasing out of high GWP refrigerants will be considerably delayed if these issues remain unresolved, and there is inadequate research on such barriers to the adoption. With an aim to investigate the precincts of limited adoption, this study identifies 18 barriers through an extensive literature survey. It also contributes to the literature by proposing two more factors responsible for the limited adoption of low GWP refrigerants in the Indian context. Interpretive structural modelling (ISM) methodology is used to elucidate the hierarchical relationship among these barriers. Lack of government incentives emerges as the most compelling factor limiting the adoption of low GWP refrigerants. The research also suggests a four-pronged strategy to promote the adoption of low GWP refrigerants in India. © 2020 Elsevier Ltd

Multichannel retailing has caused significant disruption in the market. The major credit for this disruption belongs to online retailing, which is proliferating at a high pace. With the immense growth, the online fashion retail sector faces many challenges as well. The majority of these challenges are attributed to the inefficiency of suppliers to deliver the right products at the right time. Thus, supplier selection becomes an important parameter for the success of the online fashion retail industry. This study focused on supplier selection based on seven main criteria, which are Operational Competency, Product Attribute, Logistic Warehousing, Ethics, Status, Business Competencies, and Versatility. An integrated Best Worst Method (BWM)–VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methodology was applied to prioritise the criteria and select the most feasible suppliers among available suppliers. Out of thirty-eight sub-criteria, those that got most preferences are Just in Time (JIT) Capabilities, Order Flexibility, Return Policies, Technical Soundness, Billing Flexibility. The results of the study disseminated theoretical and managerial implications that can be applied in online fashion retail. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Document co-citation analysis (DCA) is employed across various academic disciplines and contexts to characterise the structure of knowledge. Since the introduction of the method for DCA by Small (J Am Soc Inf Sci 24(4):265–269, 1973) a variety of modifications towards optimising its results have been proposed by several researchers. We recommend a new approach to improve the results of DCA by integrating the concept of the document similarity measure into it. Our proposed method modifies DCA by incorporating the semantic similarity using latent semantic analysis for the abstracts of the top-cited documents. The interaction of these two measures results in a new measure that we call as the semantic similarity adjusted co-citation index. The effectiveness of the proposed method is evaluated through an empirical study of the tourism supply chain (TSC), where we employ the techniques of the network and cluster analyses. The study also comprehensively explores the resulting knowledge structures from both the methods. The results of our case study suggest that the clustering quality and knowledge map of the domain can be improved by considering the document similarity along with their co-citation strength. © 2020, Akadémiai Kiadó, Budapest, Hungary.

Information and Communication Technologies for Development is an interdisciplinary area of research associated with engineering, application, and adoption of ICTs in developing regions and/or for development. The International Conference on Information and Communication Technologies and Development (ICTD) was started in 2006 with the objective to build a community of scholars and practitioners from diverse disciplinary backgrounds. In this paper, we examine the social dimension of ICTD as manifest in co-authorship ties in the papers published in conference proceedings. This research community has 1053 unique authors from 302 institutions in 55 countries. Almost 85% of 456 papers are co-authored by two or more people. Initially, the research community displayed small-world characteristics but the social network subsequently displays a distinct core-periphery structure. Further, collaborative ties among academic institutions in developing countries are comparatively less. A key implication is that institutional support is imperative to initiate and maintain collaborative research ties. © 2019 Commonwealth Secretariat.

Purpose: This research aims to develop a new generic framework for estimating different maintenance costs (preventive, corrective and conditional based) and its distribution to original equipment manufacturer (OEM), customer and supply chain due to no fault found (NFF) events. The study extend the domain of NFF to military aircraft maintenance, repair and overhaul (MRO) by including broader range of cost drivers than are normally found in maintenance NFF literature. Design/methodology/approach: The research applies the soft system methodology involving 80 field surveys and five in depth semi-structured interviews with practicing experts having background in military aircraft NFF MRO. For impact analysis, authors have used an agent-based model to represent and prioritize the critical NFF cost drivers during aircraft MRO based on the cost inputs of 21 technology transfer cases. Findings: The paper provides imperial insights about how NFF cost drivers affect the OEM, customer and supply chain. It suggests that NFF cost need to be part of the commercial MRO contract, depending on its frequency pattern in different types of maintenances. Research limitations/implications: The context of the current research is military aircrafts industry and may lack generalizability to commercial aircrafts. Therefore, researchers are encouraged to test the proposed propositions further. Practical implications: The developed framework will provide invaluable help in key financial decision-making during signing of MRO contract in technology transfer cases. Originality/value: This paper proposed a new prediction model for NFF cost estimation across its shareholders and current status of NFF in military aircraft NFF MRO in India. © 2020, Emerald Publishing Limited.

There is shortcoming in studying tourist destination loyalty without considering the types of tourists and heritage destinations simultaneously. Hence the present study intends to understand the type of positioning to be employed across hedonic vs utilitarian destinations so that overall destination loyalty can be enhanced. To attain the above objective data was collected from 836 tourists across two UNESCO world heritage sites. Survey data was collected and was analyzed using quantitative technique like SEM and CFA. The findings suggest that, (i) heritage destination perceived as hedonic (or utilitarian) still has the potential to offer utilitarian (or hedonic) benefits. Perception of destination image in case of familiar destination can change through positioning. However, utilitarian positioning of any destination type fails to leverage the overall image, emotional bonding and loyalty. The present study is one of its kind to explore how positioning can change brand image and improve destination loyalty. Practical implications are provided for Heritage destination marketer and managements for managing and positioning of the heritage sites. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: The sample size of 378 may not be a proper representation of a country like India, which has huge diversity within its vast population. Practical implications: 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. Originality/value: 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. © 2020, Emerald Publishing Limited.

Recent years have been transformational for organisations owing to growing generational diversity and the associated challenges of managing the different generations working together. This issue of generational diversity invites significant attention in the context of studying organisations that are going through massive transformations with the presence of different generations within the organisations at the same time (Baby Boomers, Generation X and Generation Y or Millennials). Each of the three generations, with their understanding of the business context, technology, and industry-specific knowledge, are influencing organisational learning landscapes. With this, both the organisations and individuals benefit from the exchange of expertise suited to the learning preferences of different generational cohorts. This qualitative research is an attempt to explore the aforementioned phenomenon by focusing on the nuances of the traits of different generations, their respective learning preferences and the dynamics of intergenerational learning in the context of Indian IT companies. Insights that emerge seek to unravel the observed facets of intergenerational learning by three diverse generations of IT Industry. © 2020 Sage Publications India Pvt. Ltd.

In an emerging economy like India, stone crushing industry for construction aggregates play a vital role for employment and economy. These industries are usually small scale and labour intensive in nature. Stone mining and crushing have a considerable impact on the biological resources as well as socio economic setting of local population. In this context, therefore, identifying and ranking the barriers that affect stone crushing operations becomes important. The present research attempts to explore various factors affecting stone crushing operations and their interactions. Interpretive structural modelling (ISM) is employed in this research to extract the interrelationships among the identified barriers and fuzzy analytic hierarchy process (F-AHP) applied to rank the barriers. This study can be taken as a reference by the decision makers while deciding the hierarchy of action necessary for effective operations of stone crushing units. © 2020 Inderscience Enterprises Ltd.

The current study presents a quantitative investigation of the literature on political marketing using a bibliometric approach. This study has analyzed 214 documents collected from the Web of Science database for the period between 1996 and 2018 using VOSviewer software and bibliometrix R package. Based on the insights gained from the analysis, the authors discuss the structural characteristics of various scientific agents involved in the publication process. The results of this study indicate that the literature on political marketing is highly fragmented and is still in its nascent stages. It further stresses the need to improve collaboration among researchers and promote this discipline within the research community. © 2019 John Wiley & Sons, Ltd.

Data are being continuously generated from various operational steps in the oil and gas industry. The recordings of these data and their proper utilization have become a major concern for the oil and gas industry. Decision making based on predictive as well as inferential data analytics helps in making accurate decisions within a short period of time. In spite of many challenges, the use of data analytics for decision making is increasing on a large-scale in the oil and gas industry. An appreciable amount of development has been done in the above area of research. Many complex problems may now be easily solved using Artificial Intelligence (AI) and Machine Learning (ML) techniques. Historical, as well as real-time data, can be assimilated to achieve higher production by gathering data from the gas/oil wells. Various analytical modeling techniques are now widely being used by the oil and gas sector to make a decision based on data analytics. This paper reviews the recent developments via applications of AI and ML techniques for efficient exploitation of the data obtained, starting from the exploration for crude oil to the distribution of its end products. A brief account of the acceptance and future of these techniques in the oil and gas industry is also discussed. Present work may provide a technical framework for choosing relevant technologies for effectively gaining the information from the large volume of data generated by the oil and gas industry. © 2020, Springer Nature B.V.

Purpose: The purpose of this paper is to test relevance of the information quality (IQ) framework in understanding quality of advertisements (ads) posted by ordinary consumers. Design/methodology/approach: The main objective of this study is to assess quality ads posted on customer-to-customer (C2C) social commerce platforms from an IQ framework. The authors deployed innovative text mining techniques to generate features from the IQ framework and then used a machine learning (ML) algorithm to classify ads into three categories ? high quality, medium quality and low quality. Findings: The results show that not all dimensions of IQ framework are important to assess quality of ads posted on the platforms. Potential buyers on these platforms look for appropriate amount of information, which is objective, concise and complete, to make a potential purchase decision. Research limitations/implications: As the research focuses on specific product categories, it lacks generalisability. Therefore, it needs to be tested for other product categories. Practical implications: The paper includes recommendation for C2C marketplaces on how to increase quality of ads posted by consumers on the platform. Originality/value: This study has focused on the user-generated content posted by ordinary consumers on the C2C commerce platform to sell used goods. Though C2C model has been developed on ads posted on C2C platforms, it can be established for brands as it provides them with an insight into latent dimensions that a consumer shall look for in an ad on social commerce platforms. © 2020, Emerald Publishing Limited.

The tourism sector, with its perishable nature of products, requires precise estimation of demand. To this effect, we propose a deep learning methodology, namely Bayesian Bidirectional Long Short-Term Memory (BBiLSTM) network. BiLSTM is a deep learning model, and Bayesian optimization is utilized to optimize the hyperparameters of this model. Five experiments using the tourism demand data of Singapore are conducted to ascertain the validity and benchmark the proposed BBiLSTM model. The experimental findings suggest that the BBiLSTM model outperforms other competing models like Long Short-Term Memory (LSTM) network, Support Vector Regression (SVR), Radial Basis Function Neural Network (RBFNN) and Autoregressive Distributed Lag Model (ADLM). The study contributes to tourism literature by proposing a superior deep-learning method for demand forecasting. © 2020 Elsevier Ltd

The study investigates the existence of beta-anomaly in the Indian equity market. A growing body of literature shows that financial markets do not reward the risk (defined in terms of standard deviation or market beta). These results (known as ‘low-risk anomaly’ and ‘low-beta-anomaly’) are quite puzzling from the theoretical perspective. This study presents an empirical test of the capital asset pricing model (CAPM) in the Indian equity market to examine the existence of low risk/low beta anomaly. The study covers 650 actively traded stocks for a period of 189 months from July 2002 to March 2018, and the Fama–MacBeth procedure has been used for testing CAPM. The beta-shorted portfolios are formulated using both equal and value-weights. The results of the study were found robust after controlling for outliers and correcting the bias in standard errors, as suggested by Petersen (in Rev Financ Stud 22(1):435–480, 2009). We confirm the presence of ‘low-beta-anomaly’ in India. A non-linear relationship was found between CAPM beta and expected returns. This relationship follows a quadratic function, where expected returns initially increase with beta and then start declining, resulting in the negative risk premium for high-beta portfolios. © 2020, Springer Japan KK, part of Springer Nature.

The facility layout is a well-planned process to assimilate equipment, human resources, and materials for processing a product most effectively. The facility layout problem (FLP) is an optimization problem that involves determining the shape and location of various departments within a facility, based on the inter-department distance and volume measures. CRAFT algorithm is one of the primary methods currently used for the optimization of facility layouts. The aim of this is analyzing and assessing various proposed layouts through this algorithm. The objective is to minimize the total layout cost. It depends on the material flow and distance between the departments. An attempt has also been made to optimize the layout through GA. © 2020, Springer Nature Singapore Pte Ltd.