Search results

This study aims to offer insight into the applications of multilevel confirmatory factor analysis (MCFA) within organizational behavior (OB) studies, specifically when using macro-level variables. The authors used the case of a scale measuring collective organizational engagement (COE) to demonstrate the advantage of using MCFA over single-level confirmatory factor analysis (CFA).

The authors used nine referent-shift items to measure the dimensions of physical, cognitive and emotional COE. MCFA and aggregated single-level CFA were conducted on the same data using Mplus V. 8.7, and the authors compared standardized parameters from the two techniques.

The results indicate a three-factor model of collective engagement with CFA and MCFA. Stronger item loadings, factor correlations and composite reliability were found in the MCFA within-level model compared to aggregated CFA model. MCFA also supported an alternate one-factor-between-three-factors-within model of COE.

This study demonstrates the application of MCFA with a compositional construct and compares MCFA to mean-aggregated single-level CFA. It presents a case for OB researchers to apply MCFA to compositional constructs as a best methodological practice.

Inefficient waste disposal technique and cement production methodology caused significant environmental impacts, leading to global warming. The purpose of the research was to invent an effective, sustainable technology to use the wastes and alternate for cement in concrete. Geopolymer technology could be the most desirable solution to use the wastes into an effective product.

The wood waste ash derived from nearby tea shops was used as an alternate binder for fly ash. The replacement of WWA with FA was varied from 0 to 100% at 10% intervals. In this research, setting and mechanical features of Geopolymer Concrete (GPC) along with Waste wood ash (WWA) was carried out. The influence of wood waste ash in the microstructure of the GPC was also assessed using scanning electron microscope and X-ray diffraction analysis.

The findings revealed that 30% replacement of wood waste ash was performed higher in all measured features. Besides, the formation of different phases was also observed with the inclusion of wood waste ash.

The demand for fly ash was increased in recent years, and the fly-based GPC has required more alkaline solution and temperature curing. Hence, there was a research gap on finding an alternative binder for fly ash.

The research novelty was to use the wood waste ash, which has inbuilt alkaline compounds on the production of sustainable geopolymer. The finding showed that the wood waste ash could be alternate fly ash that eliminates the environmental impacts and economic thrust.

Thin coatings are of great importance to minimize corrosion attack of steel in different environments. A review of recent work on electrodeposition and corrosion performance of Zn-Ni-based alloys for sacrificial corrosion protection of ferrous substrates is presented. The purpose of this study is to provide a systematic comparison of the corrosion resistances of Zn-Ni alloy coatings. The review contains key and outstanding comparisons of references for the period from 2007 to 2017. Binary and ternary Zn-Ni-based alloys were compared and contrasted to provide a good knowledge basis for selection of best coating system to steel substrates.

This article is a review article.

Zn-Ni-(X) alloys show great potential for replacing Cd metal in corrosion protection of steel substrates.

The research on plating of binary Zn-Ni alloys from aqueous electrolytes is now well advanced and these alloys show improved corrosion resistance compared to pure Zn. Pulse plated and compositionally modulated multilayer Zn-Ni alloy coatings showed enhanced corrosion properties compared to direct plated Zn-Ni coatings of similar composition.

The work on electrodeposition of Zn-Ni based alloys from ionic liquids is still scarce, yet these liquids show great promise in improving corrosion resistance and reducing coating thickness when compared to aqueous electrolytes. Advanced plating techniques in ionic liquids such as electromagnetic, compositionally modulated multilayer, pulse plating, ternary alloys and composites should be considered as these electrolytes avoid water chemistry and associated defects.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.