Multi-stage semi-open queuing networks (SOQNs) are widely used to analyze the performance of multi-stage manufacturing systems and automated warehousing systems. While there are several methods available for solving single-stage SOQNs, solution methods for multi-stage SOQNs are limited. Decomposition of a multi-stage SOQN into single-stage SOQNs and evaluation of an individual single-stage SOQN is a possibility. However, the challenge lies in obtaining the job departure process information from an upstream single-stage SOQN to evaluate the performance of a downstream single-stage SOQN. In this paper, we propose a two-moment approximation approach for estimating the squared coefficient of variation of the job inter-departure time from a single-stage SOQN, which can serve as an input to link multi-stage SOQNs. Using numerical experiments, we test the robustness of the proposed approach for various input parameter settings for both single and multi-class jobs. We find that the proposed approach works quite well, particularly when the coefficient of variation of the job inter-arrival time is less than two. We demonstrate the efficacy of the proposed approach using a case study on a multi-tier shuttle-based compact storage system and benchmark our results with an existing approach. The results indicate that our approach yields more accurate estimates of the performance measures in comparison to the existing approach in the literature. © 2020 Elsevier Ltd

Multi-stage semi-open queuing networks (SOQNs) are widely used to analyze the performance of multi-stage manufacturing systems and automated warehousing systems. While there are several methods available for solving single-stage SOQNs, solution methods for multi-stage SOQNs are limited. Decomposition of a multi-stage SOQN into single-stage SOQNs and evaluation of an individual single-stage SOQN is a possibility. However, the challenge lies in obtaining the job departure process information from an upstream single-stage SOQN to evaluate the performance of a downstream single-stage SOQN. In this paper, we propose a two-moment approximation approach for estimating the squared coefficient of variation of the job inter-departure time from a single-stage SOQN, which can serve as an input to link multi-stage SOQNs. Using numerical experiments, we test the robustness of the proposed approach for various input parameter settings for both single and multi-class jobs. We find that the proposed approach works quite well, particularly when the coefficient of variation of the job inter-arrival time is less than two. We demonstrate the efficacy of the proposed approach using a case study on a multi-tier shuttle-based compact storage system and benchmark our results with an existing approach. The results indicate that our approach yields more accurate estimates of the performance measures in comparison to the existing approach in the literature. © 2020 Elsevier Ltd

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 paper aims to critically analyze one of the most impactful events reported from the Indian corporate scenario in recent years, from the premise of its aesthetic underpinnings. Our focus is on the ambitious 2008 all cash cross-border acquisition of Jaguar and Land Rover businesses by Tata Motors Limited from Ford Motor Company, US. This move not only added stature to the already reputed brand but was also instrumental in positioning India in the global automotive arena. Using the Natyasastra, an ancient Sanskrit scripture on Indian dramaturgy, as an aesthetic tool, we attempt to unravel the emotional performance depicted by various stakeholders partaking in the theatrics of the Jaguar Land Rover acquisition event. In the backdrop of theatrical performance, we examine the dialogical exchange between three distinct sets of actors, the firm, media, and equity market, to identify and elucidate the emotions they predominantly depict in the Jaguar Land Rover acquisition. This paper’s over-arching goal is to shift the preoccupation with metrics of organizational performance to focus instead on the emotional performance manifested through organizational activities, using an aesthetic and essentially Indian performative perspective. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.

In this paper, we study the hub location problem of an entrant airline that tries to maximize its share in a market with already existing competing players. The problem is modeled as a non-linear integer program, which is intractable for off-the-shelf commercial solvers, like CPLEX and Gurobi, etc. Hence, we propose four alternate approaches to solve the problem. The first among them uses the Kelley's cutting plane method, the second is based on a mixed integer second order conic program reformulation, the third uses the Kelley's cutting plane method within Lagrangian relaxation, while the fourth uses second order conic program within Lagrangian relaxation. On the basis of extensive numerical tests on well-known datasets (CAB and AP), we conclude that the Kelley's cutting plane within Lagrangian relaxation is computationally the best. It is able to solve all the problem instances of upto 50 nodes within 1% optimality gap in less than 10 minutes of CPU time. © 2020 Elsevier B.V.

The content generation strategy of a sports franchise determines whether the user engagement increases or decreases on social media platforms. Thus, the role of Chief Operating Officer (COO) is profound who generally decides and governs social media policies of the franchises. We show that the cultural differences between local-COO vis-à-vis foreign-COO-governed sports franchises reflect in their content generation strategy and are also associated with user engagement. We use Hofstede's cultural dimensions theory and extract relevant features from the tweets. Overall, the results show that user engagement is more when the content generation strategy is in alignment with fans’ national culture. The first contribution of our work is towards showing the incremental impact of power distance, individualism and collectivism on user engagement. The second contribution of our work is towards feature construction, feature selection and building authorship attribution classifiers to understand the content generation strategy. Prior literature shows that national culture impacts writing of online reviews. We investigate the role of national culture in social media content generation and user engagement and extend the literature. Our study is useful for organizations to understand the role of national culture in content generation and how it is related to user engagement. © 2020 Elsevier 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.

Operations research (OR) has been at the core of decision making since World War II, and today, business interactions on different platforms have changed business dynamics, introducing a high degree of uncertainty. To have a sustainable vision of their business, firms need to have a suitable decision-making process at each stage, including minute details. Our study reviews and investigates the existing research in the field of decision support systems (DSSs) and how artificial intelligence (AI) capabilities have been integrated into OR. The findings of our review show how AI has contributed to decision making in the operations research field. This review presents synergies, differences, and overlaps in AI, DSSs, and OR. Furthermore, a clarification of the literature based on the approaches adopted to develop the DSS is presented along with the underlying theories. The classification has been primarily divided into two categories, i.e. theory building and application-based approaches, along with taxonomies based on the AI, DSS, and OR areas. In this review, past studies were calibrated according to prognostic capability, exploitation of large data sets, number of factors considered, development of learning capability, and validation in the decision-making framework. This paper presents gaps and future research opportunities concerning prediction and learning, decision making and optimization in view of intelligent decision making in today’s era of uncertainty. The theoretical and managerial implications are set forth in the discussion section justifying the research questions. © 2021, Springer Science+Business Media, LLC, part of Springer Nature.

The purpose of this study is to assess the management of bamboo across the gradient of government and community-managed forests in Maharashtra, a leading Central-Indian state in decentralized forest governance. Over the last few decades, new right-based legislations have paved the way for decentralizing forest governance in India. We first pioneered the multi-stakeholder co-production of criteria and indicators to assess the sustainability of bamboo management. Following this, the sustainability assessment was carried out using mixed methods combining vegetation surveys, focus group discussions and secondary records. We could not detect a significant role of governance in determining bamboo health across governance systems. Instead, sites with favourable locality and biotic factors supported a healthy bamboo crop. We found that while government institutions maximized financial efficiency, community institutions performed better on delivering livelihood benefits and participatory decision making. We could not find evidence of large scale over-harvesting in the community-managed forests. On the contrary, less than 5% of the bamboo potential in these villages was harvested. Traditional bamboo management across the governance gradient focused largely on production aspects. Graduating to sustainable bamboo management will require better protection, resource augmentation, sustainable harvest, enhancing livelihood benefits and creating new bulk markets. © 2020 Taylor & Francis Group, LLC.

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)

The present study extends research on the role of celebrity endorsements in destination marketing by exploring various facets of the effect of celebrity endorsements in destination marketing on the consumer. More specifically, theories of source credibility, congruence, social identity and consumer cosmopolitanism, are used to build research questions that investigate the relative effectiveness of a celebrity endorsed tourism advertisement vis a vis a generic advertisement and the boundary conditions governing the same such as destination type (local/global), celebrity country of origin and consumer level factors. The research questions are addressed using four experimental studies in sequence. The same four experiments are run in three countries with different socio-cultural backgrounds to enhance generalization, with a combined sample size of 1073 respondents. Major findings suggest that a celebrity endorser is effective for a destination advertisement. Significant cross-country differences were observed in consumer affect depending on the choice of celebrity (local or global) and the destination type (i.e., domestic or international). The effects are also moderated by consumer cosmopolitanism. The study has multiple theoretical and managerial implications. © 2020 Elsevier Ltd

With the presence of fake reviews on e-commerce platforms, the reliability of reviews becomes questionable. The extant literature demonstrates the impact of fake reviews on product sales and proposes several algorithms to prevent fake reviews from being displayed on the platform. However, what has largely remained uninvestigated is how customers perceive reviews present on the e-commerce platform. Based on the speech act theory, we develop a theoretical framework that explains how the linguistic style (both at the word and the structural level) acts as a cue for assessing a reviewer's (in)sincere intentions. We evaluate the framework on a corpus of 120 online product reviews – each examined by at least 50 customers – using the fractional logit model. Results suggest that the communication style of a speaker reflects his/her intention. Reviews with less contextual embedding, argument structuring, and flattering through non-verbal cues trigger customers towards perceiving a review as deceptive. © 2020 Elsevier Ltd

Successful new product development projects and extant research literature advocate for inclusion of inputs pertaining to the supply chain at early stages of product development to proactively identify risk averse product design concepts. To this end, we devise an analytical framework to converge upon product design concept(s) that would be associated with lesser supply chain risks, usually function of both technical and commercialization considerations. The high-level and constituent lower-level supply chain risks are represented by parent and root nodes respectively within the devised Bayesian network driven research framework. Thereafter, a quantitative measure denoted as SCRI (supply chain risk index) is evolved that yields overall composite risk numbers corresponding to respective design concepts at different risk states. Validation and comparison of the devised method with an extant study illustrates the consistency and reliability of the study. It is found that the risk propensity of a particular design concept is inversely related to the probabilistic utility of that particular concept. The case of a construction power tool of a global firm is used to demonstrate the methodology. Our research addresses an important future research pathway as argued by Hosseini et al. (2020) that extant research literature is devoid of decision-making frameworks focused on measurement and analysis the propagation of risks on complex networks. © 2020 Elsevier Ltd

Successful new product development projects and extant research literature advocate for inclusion of inputs pertaining to the supply chain at early stages of product development to proactively identify risk averse product design concepts. To this end, we devise an analytical framework to converge upon product design concept(s) that would be associated with lesser supply chain risks, usually function of both technical and commercialization considerations. The high-level and constituent lower-level supply chain risks are represented by parent and root nodes respectively within the devised Bayesian network driven research framework. Thereafter, a quantitative measure denoted as SCRI (supply chain risk index) is evolved that yields overall composite risk numbers corresponding to respective design concepts at different risk states. Validation and comparison of the devised method with an extant study illustrates the consistency and reliability of the study. It is found that the risk propensity of a particular design concept is inversely related to the probabilistic utility of that particular concept. The case of a construction power tool of a global firm is used to demonstrate the methodology. Our research addresses an important future research pathway as argued by Hosseini et al. (2020) that extant research literature is devoid of decision-making frameworks focused on measurement and analysis the propagation of risks on complex networks. © 2020 Elsevier Ltd

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

This study investigates the effects of economic policy and financial market uncertainties on Indian imports. For this purpose, we consider a panel of 97 commodities imported to India during the period: September 2011 to January 2019. We utilize two panel estimation techniques, the Pooled Mean Group (PMG) and Cross-sectionally Augmented Distributed Lag (CS-DL), for the analyses. In the short-run, we find that economic uncertainty leads to more imports to India. Conversely, in the long-run, it has a dampening effect. Our estimates also reveal that both domestic and global economic uncertainties have a considerable impact on Indian imports. However, we do not find any noticeable impact of financial market uncertainty on the imports. For robustness purposes, we also make use of aggregated import data for a longer time-horizon. These results fairly validate the findings of the commodity-level analysis. Finally, our sectoral-analysis suggests that the imports of primary products are more sensitive to the policy uncertainty than those of the manufacturing products. Given that, our study offers detailed policy suggestions in the context of an emerging economy. © 2020 Elsevier B.V.

Decision support systems (DSS) have evolved significantly since the past 50 years. The existing bouquet of DSS contributions offering the prevalent emphasis and future orientations of the field also point towards several shortcomings. The existing conceptualizations of DSS offer a fragmented portrayal of the field, with a demarcation of the academia and the industry. The literature also presented a disjoint representation of the tenets of DSS. We address these concerns in this research by synthesizing the conceptual elements of DSS towards a coherent understanding of the field. We resort to an automated content analysis procedure using text mining in an open-source platform. Lexical analysis, topic modeling, and other data mining techniques were used to unveil the latent elements of DSS. Our findings indicate the three underlying themes of DSS as 'Plan', 'Design', and 'Use'. We further identified the elements of pivotal importance of DSS that helped us in re-conceptualizing the understanding of DSS. Our validation of the notion of DSS based on the practitioner's viewpoints also attended to the issue of the academia-industry divide in terms of the perception of DSS. Further, we propose an extension of the ‘Plan-Design-Use’ model through a feedback loop based on which we present the future design possibilities in DSS in terms of the reusability of the extant DSS contributions. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

Objective: We define prediction bias as the systematic error arising from an incorrect prediction of the number of positive COVID cases x-weeks hence when presented with y-weeks of prior, actual data on the same. Our objective is to investigate the importance of an exponential-growth prediction bias (EGPB) in understanding why the COVID-19 outbreak has exploded. To that end, our goal is to document EGPB in the comprehension of disease data, study how it evolves as the epidemic progresses, and connect it with compliance of personal safety guidelines such as the use of face coverings and social distancing. We also investigate whether a behavioral nudge, cost less to implement, can significantly reduce EGPB. Rationale: The scientific basis for our inquiry is the received wisdom that infectious disease spread, especially in the initial stages, follows an exponential function meaning few positive cases can explode into a widespread pandemic if the disease is sufficiently transmittable. If people suffer from EGPB, they will likely make incorrect judgments about their infection risk, which in turn, may lead to reduced compliance of safety protocols. Method: To collect data on prediction bias, we ran an incentivized, experiment on a global, online platform with participation from people in forty-three countries, each at different stages of progression of COVID-19. We also constructed several indices of compliance by surveying participants about their frequency of hand-washing and use of sanitizers and masks; their willingness to pay for masks; their view about the social appropriateness of others’ behavior; and their like/dislike of government responses. The prediction data was used to construct several measures of EGPB. Our experimental design permits us to identify the root of under-prediction as EGPB arising from the general tendency to underestimate the speed at which exponential processes unfold. Results: Respondents make predictions about the path of the disease using a model that is substantially less convex than the actual data generating process. This creates significant EGPB, which, in turn, is significantly and negatively associated with non-compliance with safety measures. The bias is significantly higher for respondents from countries at a later stage relative to those at an early stage of disease progression. A simple behavioral nudge that shows prior data in terms of raw numbers, as opposed to a graph, causally reduces EGPB. Conclusion: Behavioral biases concerning the comprehension of disease data are quantitatively important, and act as severe impediments to effective policy action against the spread of COVID-19. Clear communication of future infection risk via raw numbers could increase the accuracy of risk perception, in turn, facilitating compliance with suggested protective behaviors. © 2020 Elsevier Ltd

We use a comprehensive data set covering almost all Indian coal-fired power plants over the period 2005–14 to evaluate the technical efficiency of power plants using the Slacks-Based Measure model. We find that average technical efficiency falls from 0.847 in 2005 to 0.742 in 2014, indicating substantial scope for efficiency improvement. This trend is driven primarily by declining energy efficiency rather than declining managerial (non-energy) efficiency. We use Simar and Wilson's bootstrapped truncated regression approach to analyze the determinants of technical efficiency. We find an inverted-U shaped relationship exists between efficiency and plant age, with maximum efficiency levels observed between 22 and 23 years of age. Privately owned plants operate at higher efficiency levels than their State-owned counterparts. Large plants are more efficient than small and medium size plants. Coal quality has no significant influence on efficiency as usage of higher calorific value coal is not accompanied by a concomitant reduction in coal use. Foreign equipped plants operate at higher efficiency levels than Indian equipped plants. © 2020 Elsevier B.V.

Financial attitude influences the financial behavior of retail investors. Although the extant research has acknowledged and examined this relationship, the measures of financial attitude and behavior still vary widely and are generally posed as a series of questions rather than statements. In addition to this, there is insufficient knowledge regarding retail investors' behavior in the face of a health crisis, such as the current COVID-19 pandemic. This study addresses these gaps in the prior literature by examining the relative influence of six dimensions of financial attitude, namely, financial anxiety, optimism, financial security, deliberative thinking, interest in financial issues, and needs for precautionary savings, on the trading activity of retail investors during the pandemic. Data were collected from 404 respondents and analyzed using the artificial neural network (ANN) method. The results revealed that all six dimensions had a positive influence on trading activity, with interest in financial issues exerting the strongest influence, followed by deliberative thinking. The study thus contributes important inferences for researchers and managers. © 2020 The Author(s)