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The paper aims to analyse the postharvest Supply Chain (SC) of tomatoes to maximise profit by restructuring the modes of operation.

System Dynamics-based simulation of four scenarios depicting different operational modes of postharvest food SC was employed. Real-time of Tamil Nadu state, India was used to enact the scenarios using Vensim.

Results indicated that cold storage improved the profit of wholesalers by prolonging the shelf-life of commodities. Retailers and farmers gained more profit in the absence of wholesalers. Though the absence of middlemen reduces the transit time and prevents deterioration, the role of wholesalers, i.e. transporting the commodities to farther customers’ needs to be shouldered by other agents effectively to minimise losses.

The accuracy of the results depend on the exactness of the data collected. The simulation findings, on the other hand, could be helpful in decision-making as these models portray the actual operational modes of postharvest SC. The suitability of each network structure depends on the capabilities of the agents, market scenario and demography. Implications based on discussions with stakeholders and in terms of establishing dedicated societies (cooperatives) have been provided.

Postharvest losses incurred for horticultural crops like tomatoes are significantly high. It is of much importance to India as agriculture contributes to 17% of the Gross Domestic Product (GDP) and India is the second-largest producer of tomatoes globally. The study would shed light on restructuring the network appropriately.

We are interested in how and why people use or take part to crowdfunding projects.

Over the past four years, we have interviewed over 120 crowdfunding requesters and supporters of over 15 project types from dance to technology to publishing.

The key contributions of this research are: An understanding of the work involved, an understanding of motivations for participation, and an understanding of how the design of platforms influences engagement.

We adopt a computer-supported cooperative work approach from sociology, computer science, and design to provide a new perspective to researchers who seek to understand user behavior, motivations, and the mechanisms in place to support engagement with crowdfunding technology.

The purpose of this paper is to develop a comprehensive framework for improving intensive care unit performance.

The study introduces a quality management framework by combining cause and effect diagram and logical framework. An intensive care unit was identified for the study on the basis of its performance. The reasons for not achieving the desired performance were identified using a cause and effect diagram with the stakeholder involvement. A logical framework was developed using information from the cause and effect diagram and a detailed project plan was developed. The improvement projects were implemented and evaluated.

Stakeholders identified various intensive care unit issues. Managerial performance, organizational processes and insufficient staff were considered major issues. A logical framework was developed to plan an improvement project to resolve issues raised by clinicians and patients. Improved infrastructure, state‐of‐the‐art equipment, well maintained facilities, IT‐based communication, motivated doctors, nurses and support staff, improved patient care and improved drug availability were considered the main project outputs for improving performance. The proposed framework is currently being used as a continuous quality improvement tool, providing a planning, implementing, monitoring and evaluating framework for the quality improvement measures on a sustainable basis.

The combined cause and effect diagram and logical framework analysis is a novel and effective approach to improving intensive care performance. Similar approaches could be adopted in any intensive care unit.

The paper focuses on a uniform model that can be applied to most intensive care units.

The purpose of this paper is to present the application of logical framework analysis (LFA) for implementing continuous quality improvement (CQI) across multiple settings in a tertiary care hospital.

This study adopts a multiple case study approach. LFA is implemented within three diverse settings, namely, intensive care unit, surgical ward, and acute in-patient psychiatric ward. First, problem trees are developed in order to determine the root causes of quality issues, specific to the three settings. Second, objective trees are formed suggesting solutions to the quality issues. Third, project plan template using logical framework (LOGFRAME) is created for each setting.

This study shows substantial improvement in quality across the three settings. LFA proved to be effective to analyse quality issues and suggest improvement measures objectively.

This paper applies LFA in specific, albeit, diverse settings in one hospital. For validation purposes, it would be ideal to analyse in other settings within the same hospital, as well as in several hospitals. It also adopts a bottom-up approach when this can be triangulated with other sources of data.

LFA enables top management to obtain an integrated view of performance. It also provides a basis for further quantitative research on quality management through the identification of key performance indicators and facilitates the development of a business case for improvement.

LFA is a novel approach for the implementation of CQI programs. Although LFA has been used extensively for project development to source funds from development banks, its application in quality improvement within healthcare projects is scant.

(1) A mathematical model for the Hybrid nanofluids flow is used as carriers for delivering drugs. (2) The flow conditions are controlled to enable drug-loaded nanofluids to flow through the smaller gap between the two tubes. (3) Hybrid nanofluids (HNFs) made from silver (Ag) and titanium dioxide (TiO₂) nanoparticles are analyzed for applications of drug delivery. (Ag) and (TiO₂) (NPs) are suitable candidates for cancer treatment due to their excellent biocompatibility, high photoactivity, and low toxicity. (4) The new strategy of artificial neural networks (ANN) is used which is machine-based and more prominent in validation, and comparison with other techniques.

The two Tubes are settled in such a manner that the gap between them is uniform. The Control Volume Finite Element Method; Rk-4 and Artificial Neural Network (ANN).

(1) From the obtained results it is observed that the dispersion and distribution of drug-loaded nanoparticles within the body will be improved by the convective motion caused by hybrid nanofluids. The effectiveness and uniformity of drug delivery to target tissues or organs is improved based on the uniform flow and uniform gap. (2) The targeting efficiency of nanofluids is further improved with the addition of the magnetic field. (3) The size of the cylinders, and flow rate, are considered uniform to optimize the drug delivery.

(1)The flow phenomena is considered laminar, one can use the same idea through a turbulent flow case. (2) The gap is considered uniform and will be interesting if someone extends the idea as non-uniform.

(1) To deliver drugs to the targeted area, a suitable mathematical model is required. (2) The analysis of hybrid nanofluids (HNFs) derived from silver (Ag) and titanium dioxide (TiO₂) nanoparticles is conducted for the purpose of drug delivery. The biocompatibility, high photoactivity, and low toxicity of (Ag) and (TiO₂) (NPs) make them ideal candidates for cancer treatment. (3) Machine-based artificial neural networks (ANN) have a new strategy that is more prominent in validation compared to other techniques.

The drug delivery model is a useful strategy for new researchers. (1) They can extend this idea using a non-uniform gap. (2) The flow is considered uniform, the new researchers can extend the idea using a turbulent case. (3) Other hybrid nanofluids flow, in the same model for other industrial usages are possible.

All the obtained results are new. The experimental thermophysical results are used from the existing literature and references are provided.

Palliative care concentrates on reducing the severity of disease symptoms, rather than providing a cure. The goal is to prevent and relieve suffering and to improve the quality of life for people facing serious, complex illness. It is therefore critical in the palliative environment that caregivers are able to make recommendations to patients and families based on reasonable assessments of amount of suffering and quality of life. This research uses statistical methods of evaluation and prediction as well as simulation to create a multiple criteria model of survival rates, survival likelihoods, and quality of life assessments. The results have been reviewed by caregivers and are seen to provide a solid analytical base for patient recommendations.

Over the years, with the increasing competition of the marketplace, the distinction between manufacturing and service industries is getting blurred. The core, manufactured products today are so entwined with services, that they have become indistinguishable. Moreover, these services are expected by the customer as an integral part of the product. Slowly all business is tending to be service‐oriented, aimed at satisfying customer needs. The Indian steel industry has been liberalised, after decades of protection and the competition is fierce. Capacities are being added at a furious pace, newer technologies are being introduced and cheaper imports are being dumped. In such a scenario, the answer to gaining competitive advantage lies in providing superior value to the customer, by providing customer service with the product at a lower delivery cost. Using customer service to retain and acquire customers could provide a new strategic advantage for steel makers. This paper explores the key issues, and possibilities and presents a strategy for achieving strategic advantage through customer service, in the context of the Indian steel industry. The concept can be extrapolated for any developing economy.

The purpose of this paper is to compare the practices of continuous improvement that are applied in medium and large manufacturing and service companies in two Latin American countries. At the same time, benefits and barriers experienced by these companies with regard to sustainability of continuous improvement are explored.

In order to generate a comparative study between two Latin American countries, interviews were conducted with managers linked to continuous improvement in medium and large companies in the State of Puebla and the Metropolitan District of Quito, which are important areas in Mexico and Ecuador, respectively. Data were collected by means of document analysis, semi-structured interviews, and direct observation.

Companies in both countries identify the use of various techniques and/or tools for continuous improvement. The results of the empirical evidence show how the impact of the application of the techniques has been beneficial in economic and human terms. Thus, the exploratory study has permitted the identification of the drivers and inhibitors in the maintenance of continuous improvement.

The research is based on only two areas of the Latin American countries: Mexico and Ecuador. Their results can therefore not be generalized. The approach is applied in a specific environment, namely, the State of Puebla and the Metropolitan District of Quito. This study incorporates the perception of managers, directors, and/or supervisors involved in continuous improvement processes.

This paper seeks to provide analytical input. The study is of great interest to researchers, managers, consultants, and professionals linked to projects of continuous improvement who wish to incorporate continuous improvement practices which are sustainable over time. A new managerial behavior is the basis of continuous improvement, where the training and development of the human resource increases the commitment to achieve organizational changes.

This research makes an empirical contribution to the literature through the understanding of practices of continuous improvement in a Latin American context, highlighting the factors that improve or impede the process of continuous improvement. Particularly in Mexico and Ecuador, the empirical evidence on this subject is still scarce despite the existence of theoretical academic literature.

The crowdfunding market has experienced rapid growth in recent years. However, not all projects are successfully financed because of information asymmetries between the founder and the providers of external finance. This shortfall in funding has made factors that lead to successful fundraising, a great interest to researchers. This study draws on the social capital theory, human capital theory and level of processing (LOP) theory to predict the success of crowdfunding projects.

A feature set is extracted and correlations between project success and features are utilized to order the features. The artificial neural network (ANN) is popularly applied to analyze the dependencies of the input variables to improve the accuracy of prediction. However, the problem of overfitting may exist in such neural networks. This study proposes a neural network method based on ensemble machine learning and dropout methods to generate several neural networks for preventing the problem of overfitting. Four machine learning techniques are applied and compared for prediction performance.

This study shows that the success of crowdfunding projects can be predicted by measuring and analyzing big data of social media activity, human capital of funders and online project presentation. The ensemble neural network method achieves highest accuracy. The investments rose from early projects and another platform by the funder serve as credible indicators for later investors.

The managerial implication of this study is that the project founders and investors can apply the proposed model to predict the success of crowdfunding projects. This study also identifies the most influential features that affect fundraising outcomes. The project funders can use these features to increase the successful opportunities of crowdfunding project.

This study contributes to apply a new machine learning modeling method to extract features from activity data of crowdfunding platforms and predict crowdfunding project success. In addition, it contributes to the research on the deployment of social capital, human capital and online presentation strategies in a crowdfunding context as well as offers practical implications for project funders and investors.

Knowledge management (KM) is associated with higher performance and innovative culture; KM can help the public sector to be fiscally lean and meet diverse stakeholders’ needs. However, hierarchical structures, bureaucratic culture and rigid processes inhibit KM adoption and generate inertia. This study aims to explore the nature and causes of this inertia within the context of the United Arab Emirates (UAE) public sector.

Using an in-depth case study of a UAE public sector organisation, this study explores how organisational inertia can be countered to enable KM adoption. Semi-structured interviews are conducted with 17 top- and middle-level managers from operational, management and strategic levels. Interview data is triangulated with content analysis from multiple sources, including the UAE Government and case organisation documents.

The results show transformation leadership, external factors and organisational culture mediate the negative effect of inertia on KM practices adoption. We find that information technology plays a key role in enabling knowledge creation, access, adoption and sharing. Furthermore, we uncover a virtuous cycle between organisational culture and KM practices adoption in the public sector. In addition, we develop a new model (the relationship between KM practices, organisational inertia, organisational culture, transformational leadership traits and external factors) and four propositions for empirical testing by future researchers. We also present a cross-case comparison of our results with six private/quasi-private sector cases who have implemented KM practices.

Qualitative data is collected from a single case study.

Inertia in a public section is a result of bureaucracy and authority bounded by the rules and regulations. Adopting a qualitative methodology and case study method, the research explores the phenomena of how inertia impacts KM adoption in public sector environments. Our findings reveal the underlying mechanisms of how internal and external organisational factors impact inertia. Internally, supportive organisational culture and transformational leadership traits positively effect KM adoption, which, in turn, has a positive effect on organisational culture to counter organisational inertia. Externally, a progressive national culture, strategy and policy can support a knowledge-based organisation that embraces change. This study develops a new model (interactions between internal and external factors impacting KM practices in the public sector), four propositions and a new two-stage process model for KM adoption in the public sector. We present a case-comparison of how the constructs interact in a public sector as compared to six private/quasi-private sector cases from the literature.