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This study focused on identifying critical factors governing the fire response of prestressed hollow-core slabs. The hollow-core slabs used as flooring units can be subjected to elevated temperatures during a fire. The fire response of prestressed hollow-core slabs is required to develop slabs with greater fire endurance. The present study aims to determine the extent to which the hollow-core slab can sustain load during a fire without undergoing progressive collapse under extreme fire and heating scenarios.

A finite element model was generated to predict the fire response of prestressed hollow core slabs under elevated temperatures. The accuracy of the model was predicted by examining thermal and structural responses through coupled temperature displacement analysis. A sensitivity analysis was performed to study the effects of concrete properties on prediction of system response. A parametric study was conducted by varying the thickness of the slab, fire and heating scenarios.

Thermal conductivity and specific heat of concrete were determined as sensitive parameters. The thickness of the slab was identified as a critical factor at a higher load level. Asymmetric heating of the slab resulted in higher fire resistance compared with symmetric heating.

This is the first study focused on studying the effect of modeling uncertainties on the system response by sensitivity analysis under elevated temperatures. The developed model with a parametric study helps in identifying critical factors for design purposes.

Allocating budget optimally to marketing channels is an increasingly difficult venture. This difficulty is compounded by an increase in the number of marketing channels, a rise in siloed data between marketing technologies, and a decrease in individually identifiable data due to legislated privacy policies. The authors explore the rich attribution modeling literature and discuss the different model types and approaches previously used by practitioners and researchers. They also investigate the changing landscape of marketing attribution, discuss the advantages and disadvantages of different data handling approaches (i.e., aggregate vs. individualistic data), and present a research agenda for future attribution research.

With ever‐increasing competitive pressures, growing numbers of firms use electronic procurement (e‐procurement) in an attempt to reduce costs and increase profitability. Academicians and practitioners alike agree that one of the most important benefits of e‐procurement is its ability to facilitate integration within the firm and across the supply chain. However, there is much to be discovered about the prevalence of actual implementation of e‐procurement. The purpose of this study is to empirically examine the extent to which firms operating in diverse industries use nine different e‐procurement tools that differ in their ability to facilitate supply chain integration. The survey data were provided by a sample of 142 members of the Institute for Supply Management (ISM). Factor analysis revealed that the group of nine e‐procurement tools could be categorized into two types: basic, single‐process tools and integrative tools. A t‐test of the mean differences between each type of e‐procurement tool revealed that firms used basic, single‐process tools to a greater extent than they used integrative forms of e‐procurement. To further explore firms’ use of e‐procurement, we attempted to ascertain whether the industry in which a firm operates impacts use. Logistic regression revealed that firm sector has an effect on the use of integrative eprocurement tools, with firms operating in the petroleum and the transportation equipment sectors being less likely to use them than their manufacturing counterparts. These findings are important, as previous research indicates that effective supply chain integration is associated with improvements in production planning, inventory management, distribution, and overall supply chain performance.

The purpose of this paper is to benchmarking the governance performance of Indian states.

This paper provides a framework to measure governance performance at the state level. Using the data on 28 key indicators, the authors evaluate Indian states on seven broad dimensions of governance quality covering several aspects of public service delivery, regulatory quality and law and order. The empirical methodology involves the application of multi-criteria decision making techniques in two steps. The authors, first develop suitable weights of the identified dimensions and criteria under each dimension by applying the inputs of an expert-based decision-panel in a best-worst framework. Next, using these weights, the authors evaluate ranking of each state using TOPSIS and PROMETHEE-II methods.

The results indicate wide disparities in the governance performance of Indian states. Based on different indicators, the paper evaluates the rank of all the major Indian states. Results reveal that “Social Service Delivery(S)” is the most influencing dimension for the development of a state. Overall, the authors find Andra Pradesh, NCT of Delhi and Goa to be the leading states in terms of governance quality.

The paper provides policy makers with easy to use operational indicators to analyse the governance performance of Indian states. These would help in better monitoring of these states through competitive goal-setting for continuous improvement.

To the best of the authors’ knowledge, this study is the first formal assessment of governance quality in the Indian states in a multi-criteria framework. To this end, the paper addresses the issue of wide regional disparities in the country. The findings of the paper provide powerful insights to policy makers in setting up appropriate strategies to eliminate these disparities.

The purpose of this paper is to reduce the post-harvest loss occurring through respiration and CO₂ emission produce by the selected produces, during logistics. This paper proposes a supply chain (SC) structure for the Indian traditional agriculture SC planning model to reduce post-harvest loss and mixed closed transportation to reduce CO₂ emission.

The Indian agriculture SC structure is modeled and solved by genetic algorithm using a MATLAB Optimization toolbox. The respiration rate is measured by a static method. These values are applied in an SC planning model and the post-harvest loss and its corresponding CO₂ emission are estimated.

This paper proposes a supply structure for the Indian traditional agriculture SC to reduce the post-harvest loss; the experiments measured the respiration rate to estimate the CO₂ emission. The mixed closed transportation method is found to be suitable for short-purpose domestic transportation.

The optimized supply structure leads to unemployment through eliminating the intermediaries. Therefore, further research encourages the conversion of intermediaries into hub instead of eliminating them.

This paper includes implications for the development of Indian traditional agriculture SC by an optimized supply structure and novel transportation method for the selected agriculture produces based on compatibility.

This paper identified that the agriculture produces respiration can also emit the CO₂. The closed transportation method can reduce the CO₂ emission of produces respiration than traditional open transportation.