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This research aims to identify the factors that influence the investment intention of retail investors in Indian REITs. The study incorporates the theory of planned behavior and innovation diffusion theory as the research framework, with perceived risk and mass media influence as additional constructs.

Primary data were collected using self-administered questionnaires from 534 potential investors in India. The data were analyzed using partial least square structural equation modeling.

The study showed that factors such as relative advantage, compatibility, attitude, subjective norms, perceived behavioral control and mass media significantly and positively influence investment intention in Indian REITs. However, perceived risk was found to have a negative and significant influence, while complexity did not affect investment intention.

This is the first quantitative investigation into determining the factors influencing the investment intention of Indian retail investors on Indian REITs.

The present study delves into the causes of relatively lower retail participation in the Indian REIT market. Specifically, it investigates investors' attitudes and perceptions towards REITs as a unique asset class. This paper provides a comprehensive understanding of the perception and factors influencing Indian retail investors' reluctance to participate in the REIT market.

Qualitative research was conducted through semi-structured interviews to gather insights from non-investors in REITs. The data were transcribed and analyzed using content analysis techniques. Finally, coding techniques were used to identify broad study themes.

According to the study results, many retail investors are unfamiliar with REITs. Even among those knowledgeable about REITs and with a favorable view, it is not commonly seen as a feasible investment option due to its early stage, unattractive returns and limited number of REITs.

Developed countries have established REIT markets, while it is still in its infancy in developing countries such as India. Financial advisors, fund houses and the media should focus on educating investors to increase awareness.

The study is the first qualitative investigation into the perception of retail investors to understand the reasons for lower retail engagement in the Indian REIT market.

In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc . In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset.

In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis.

In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like “Threshold segmentation” and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained.

The implemented machine learning design is outperformance methodology, and they are proving good application detection rate.

The purpose of this paper is to review the accreditation research in specific to its effect on the performance of healthcare organizations.

A comprehensive search and analysis of literature on the effect of healthcare accreditation were conducted between June 2017 and May 2018. The study identified 62 empirical research studies that examined the effect of healthcare accreditation programmes. Study particulars such as year of publication, objectives, focus of the study, research settings and key findings were recorded. A content analysis was performed to identify the frequency of the main themes in the literature. Knowledge gaps needing further examination were identified.

Majority of the accreditation impact studies were carried out in the developed nations (n = 49). The thematic categories, that is the impact on “patient safety and healthcare quality” (n = 26), “healthcare professionals’ views” (n = 28) and “clinical process and outcomes” (n = 17) were addressed more times. Whereas the other two thematic categories “organizational performance” and “consumers’ views or satisfaction,” each was examined less than 10 instances. This review reveals mixed views on effect of healthcare accreditation. The varied quality of studies and the availability of a few studies on consumers’ perception of accreditation effectiveness were the important limiting factors of this review.

The findings are valuable to healthcare managers and hospital administrators in accreditation decisions, whereas findings are of value to researchers and academicians in terms of gaps identified for future research studies pertaining to the impact of healthcare accreditation. Future studies need to consider holistic theoretical frameworks for assessing the effect of accreditation on performance of healthcare organizations to achieve precise results.

The purpose of this paper is to study the various solutions and recommendations provided by researchers in applying the blockchain concept to different problems in aviation industry. It will discuss and highlight the specific approaches that leverages blockchain to mitigate the automatic dependent surveillance-broadcast (ADS-B) security issues. Furthermore, it introduces an innovative design and method to secure ADS-B data using a tamper-resistant distributed public ledger of authenticated flight plans and validates the position and other parameters of the associated aircraft identifier against the flight paths/routes that are stored in the blockchain ledger.

ADS-B is the key technology that is mandated by Federal Aviation Administration in USA by 2020. However, ADS-B data is neither encrypted nor authenticated. This paper proposes a novel solution using blockchain to secure the ADS-B data communications and in-depth analysis of existing solutions covering the following aspects: classification of various possible attacks on ADS-B. Presents various solutions proposed by different researchers regarding use of blockchain in aviation industry. Discuss a new solution to secure ADS-B using blockchain. Discuss the high-level architectural framework of the proposed and patented solution. Finally, presents the conclusions and future work scope.

While the main intention of this paper is to bring together all the existing solutions using blockchain to secure aviation and ADS-B data at one place, the proposed novel solution could contribute to maintaining security and privacy for aircrafts flying in the airspace at any point in time. Continuously securing the ADS-B data transmissions based upon the filed flight plans in real time can provide a mechanism to identify spoofed aircraft messages and communicate the same to ground stations for authentication of existence of such a malicious aircraft. Thus, this solution also differs from all the existing ones.

As aviation industry is in its infancy stage in implementing blockchain-based solutions, practical implementation of the proposed concept might take longer.

This paper is a comprehensive survey and review paper. To the best of the authors’ knowledge, this is the first of its kind that presents various use cases for usage of blockchain in aviation industry along with a detailed review of existing proposed or implemented solutions using blockchain to secure ADS-B data. This can serve as an invaluable reference for the future researchers on this topic in both industry and academia.

Friction stir processing (FSP) is overviewed with the process variables, along with the thermal aspect of different metals.

With its inbuilt advantages, FSP is used to reduce the failure in the structural integrity of the body panels of automobiles, airplanes and lashing rails. FSP has excellent process ability and surface treatability with good corrosion resistance and high strength at elevated temperatures. Process parameters such as rotation speed of the tool, traverse speed, tool tilt angle, groove design, volume fraction and increase in number of tool passes should be considered for generating a processed and defect-free surface of the workpiece.

FSP process is used for modifying the surface by reinforcement of composites to improve the mechanical properties and results in the ultrafine grain refinement of microstructure. FSP uses the frictional heat and mechanical deformation for achieving the maximum performance using the low-cost tool; the production time is also very less.

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This paper aims to utilize an extended involvement-commitment and trust commitment model to examine post-consumption decisions related to food delivery app use.

A self-administered online survey was used to collect data from food delivery app users in the USA.

Findings validate a favorable role of perceived app security and menu description on trust in app recommendations. Trust was found to be positively related to involvement, commitment and willingness to provide feedback. The positive moderating role of perceived convenience and rewards and incentives was also confirmed in relation to consumers’ trust in app recommendations, and involvement and commitment

A key contribution of this study includes the development of a comprehensive model to understand postconsumption decisions related to the usage of food delivery apps. To the best of the authors’ knowledge, this study is also the first to unveil the antecedent and moderating factors related to food delivery app users’ willingness to provide feedback, share personal data and to pay more.

This paper aims to empirically analyze the impact of export diversification on gross domestic product (GDP) per capita growth.

Using system generalized method of moments (GMM), a nonlinear model in a dynamic panel data growth framework for 101 countries between 1995 and 2019 was estimated.

Results evidenced that export concentration, measured by the Herfindahl–Hirschman Index (HHI), is negatively associated with GDP per capita growth after controlling for the effects of other explanatory variables. Further, the squared term of HHI used in the model to measure the nonlinear relationship between export concentration and economic growth indicated that the low-income and lower-middle-income countries benefited from export diversification. At the same time, high-income and upper-middle-income countries perform well with their export specialization. The results of the robustness check validate the findings of nonlinear estimation.

The findings recommend that low-income and lower-middle-income countries diversify their export basket to improve economic growth by generating stable export earnings. Similarly, high-income and upper-middle-income countries should focus on measures to close the product lines which no longer belong to their factor endowments and rebalance their export basket.

This study contributes to the existing literature by using the system GMM method, which is most appropriate for a dynamic panel data growth framework with up-to-date data. Further, this study segregates a large panel into 43 concentrated and 58 diversified countries to test the robustness of the empirical results.

This study investigates the relationships among three types of employees’ perceived job control, employee commitment, employees’ well-being, and job performance.

The proposed relationships were investigated based on a cross-section, online, self-administered survey.

The results confirmed the positive role of employees’ job control perceptions on work-related responses.

Results suggested hospitality managers implement practices to improve job control perceptions of employees to enhance their well-being and job performance.

This study was the first to investigate the roles of three types of job control perceptions on employees’ well-being and commitment, based on positive organizational behavior literature and control theory. This study was also the first attempt to explore three types of job control focusing on employees’ perspectives in the hospitality industry.

The corrosion of cupronickel and copper alloys in marine and chloride environments presents significant challenges in the chemical and petrochemical industries. This paper aims to investigate the corrosion inhibition of cupronickel alloy (Cu-10Ni) in a sodium chloride medium using expired amlodipine as a corrosion inhibitor. The use of this drug in its expired form could reduce the costs of corrosion and help mitigate the accumulation of pharmaceutical waste.

The inhibitory action was evaluated using a weight loss method, potentiodynamic polarization, electrochemical impedance spectroscopy measurements, scanning electron microscopy (SEM) and atomic force microscopy (AFM). The effect of temperature on the inhibition performance was also studied.

The results of these experiments demonstrated that the drug amlodipine effectively inhibited the corrosion of cupronickel alloy in chloride solutions. The corrosion rate of cupronickel was found to decrease with increasing inhibitor concentration and to increase with rising temperature. A maximum inhibition efficiency of 91.92 was achieved with an inhibitor concentration of 0.025 g/L at 298 K. Adsorption of the inhibitor followed the Langmuir adsorption isotherm. Polarization studies indicated that the expired drug acted as a mixed inhibitor. SEM and AFM analyses confirmed that the surface morphology of cupronickel specimens was significantly improved in the presence of the inhibitor.

Amlodipine can be conveniently used to mitigate problems with the corrosion of copper alloys in chloride environments.

Amlodipine is evaluated as a novel and effective corrosion inhibitor for cupronickel alloy in neutral chloride environments.