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The purpose of this study is to explain the poor informativeness of earnings in dual-class firms by examining the quality of earnings and the information environment.

The earnings informativeness, earnings quality and information environment of dual-class firms are compared with a matched sample of single-class firms. The authors have performed the returns-earnings association tests, examine the quality of earnings by using proxies for discretionary accruals, and examine the information environment by employing four empirical constructs: the analyst forecast dispersion, absolute forecast errors, Amihud’s (2002) illiquidity measure, and the bid-ask spread.

The results show that the quality of earnings is better while the quality of the information environment is worse in dual-class firms compared to single-class firms. Overall, the results suggest that an inferior information environment is a plausible explanation for the low informativeness of dual-class firms’ earnings.

The results provide empirical support for Dechow et al. (2010) that the use of the earnings-returns association measure to draw conclusions about the quality of earnings is not appropriate in the presence of a poor information environment.

This is the first study to empirically show that low earnings informativeness in dual-class firms can be explained by the inferior quality of the information environment.

There is empirical evidence that a firm's addition to S&P 500 results in significant abnormal returns and an increase in a stock's liquidity. The purpose of this paper is to argue that changes in the information environment after the year 2000 due to the implementation of Regulation Fair Disclosure (FD), decimalization and Sarbanes Oxley Act, should result in reduced abnormal returns in the post‐2000 period.

The authors compare the abnormal returns and liquidity changes around the announcement day of firm's addition to S&P 500 in the pre‐ and post‐2000 periods. Univariate and multivariate tests are used to control for factors that research shows affect the abnormal returns around additions to S&P 500.

It is found that the reduction in informational asymmetry in the post‐2000 period has resulted in a significant decrease in the abnormal return on the announcement day of additions to S&P 500 index and changes in the stock's liquidity in the post announcement period are now marginal.

Existing literature related to changes in the abnormal returns around additions to S&P 500 does not account for changes in the information environment in the two sub periods, pre‐ and post‐2000. The results may have implications for studies related to additions to S&P 500 where the sample period spans over the two sub periods.

The adoption of climate-smart agricultural practices (CSAPs) is important for sustaining Indian agriculture in the face of climate change. Despite considerable effort by both national and international agricultural organizations to promote CSAPs in India, adoption of these practices is low. This study aims to examine the elements that affect the likelihood and intensity of adoption of multiple CSAPs in Bihar, India.

The probability and intensity of adoption of CSAPs are analyzed using multivariate and ordered probit models, respectively.

The results show significant correlations between multiple CSAPs, indicating that their adoptions are interrelated, providing opportunities to exploit the complementarities. The results confirm that both the probability and intensity of adoption of CSAPs are affected by numerous factors, such as demographic characteristics, farm plot features, access to market, socio-economics, climate risks, access to extension services and training. Farmers who perceive high temperature as the major climate risk factor are more likely to adopt crop diversification and minimum tillage. Farmers are less likely to adopt site-specific nutrient management if faced with short winters; however, they are more likely to adopt minimum tillage in this case. Training on agricultural issues is found to have a positive impact on the likelihood and the intensity of CSAPs adoption.

The major policy recommendations coming from of our results are to strengthen local institutions (public extension services, etc.) and to provide more training on CSAPs.

By applying multivariate and ordered probit models, this paper provides some insights on the long-standing discussions on whether farmers adopt CSAPs in a piecemeal or in a composite way.

The aim of this paper is to study the effect of laser shock peening (LSP) on mechanical behaviour of the laser-directed energy deposition (LDED)-based printed 15-5 PH stainless steel with U and V notches. The study specifically concentrates on the evaluation of effect of scan strategy, machining and LSP processing on microstructural, texture evolution and fatigue behaviour of LDED-printed 15-5 PH steel.

For LSP treatment, 15-5 PH steel was printed using LDED process with bidirectional scanning strategy (XX [θ = 0°) and XY [θ = 90°]) at optimised laser power of 600 W with a scanning speed of 300 mm/min and a powder feed rate of 3 g/min. Furthermore, LSP treatment was conducted on the V- and U-notched fatigue specimens extracted from LDED-built samples at laser energy of 3.5 J with a pulse width of 10 ns using laser spot diameter of 3 mm. Post to the LSP treatment, the surface roughness, fatigue life assessment and microstructural evolution analysis is performed. For this, different advanced characterisation techniques are used, such as scanning electron microscopy attached with electron backscatter diffraction for microstructure and texture, X-ray diffraction for residual stress (RS) and structure information, Vicker’s hardness tester for microhardness and universal testing machine for low-cycle fatigue.

It is observed that both scanning strategies during the LDED printing of 15-5 PH steel and laser peening have played significant role in fatigue life. Specimens with the XY printing strategy shows higher fatigue life as compared to XX with both U- and V-notched conditions. Furthermore, machining and LSP treatment led to a significant improvement of fatigue life for both scanning strategies with U and V notches. The extent of increase in fatigue life for both XX and XY scanning strategy with V notch is found to be higher than U notch after LSP treatment, though without LSP samples with U notch have a higher fatigue life. As fabricated sample is found to have the lowest fatigue life as compared to machines and laser peened with both scan strategies.

This study presents an innovative method to improve the fatigue life of 15-5 PH stainless steel by changing the microstructure, texture and RS with the adoption of a suitable scanning strategy, machining and LSP treatment as post-processing. The combination of preferred microstructure and compressive RS in LDED-printed 15-5 PH stainless steel achieved with a synergy between microstructure and RS, which is responsible to improve the fatigue life. This can be adopted for the futuristic application of LDED-printed 15-5 PH stainless steel for different applications in aerospace and other industries.

Many investigations are going on in monitoring, contact tracing, predicting and diagnosing the COVID-19 disease and many virologists are urgently seeking to create a vaccine as early as possible. Even though there is no specific treatment for the pandemic disease, the world is now struggling to control the spread by implementing the lockdown worldwide and giving awareness to the people to wear masks and use sanitizers. The new technologies, including the Internet of things (IoT), are gaining global attention towards the increasing technical support in health-care systems, particularly in predicting, detecting, preventing and monitoring of most of the infectious diseases. Similarly, it also helps in fighting against COVID-19 by monitoring, contract tracing and detecting the COVID-19 pandemic by connection with the IoT-based smart solutions. IoT is the interconnected Web of smart devices, sensors, actuators and data, which are collected in the raw form and transmitted through the internet. The purpose of this paper is to propose the concept to detect and monitor the asymptotic patients using IoT-based sensors.

In recent days, the surge of the COVID-19 contagion has infected all over the world and it has ruined our day-to-day life. The extraordinary eruption of this pandemic virus placed the World Health Organization (WHO) in a hazardous position. The impact of this contagious virus and scarcity among the people has forced the world to get into complete lockdown, as the number of laboratory-confirmed cases is increasing in millions all over the world as per the records of the government.

COVID-19 patients are either symptomatic or asymptotic. Symptomatic patients have symptoms such as fever, cough and difficulty in breathing. But patients are also asymptotic, which is very difficult to detect and monitor by isolating them.

Asymptotic patients are very hazardous because without knowing that they are infected, they might spread the infection to others, also asymptotic patients might be having very serious lung damage. So, earlier prediction and monitoring of asymptotic patients are mandatory to save their life and prevent them from spreading.

This study aims to examine whether or not residential properties closer to landfill sites have lower offer values by the developers. That is, by analyzing real estate data and landfill site locations, the study seeks to provide insights into whether properties situated closer to landfill sites tend to have a lower offer values than those located farther away.

The study is exploratory in nature, and a case study approach is applied. A landfill site named “Uruli Devachi” is selected in the region of Pune district, and data is collected from 102 developers selling residential projects within a radius of 15 km (about 9.32 mi). The gathered data is analyzed by using basic descriptive statistics, one-way ANOVA and ordinary least squares (OLS) regression. The OLS regression helps to determine whether there is a relationship between the distance of a residential property from a landfill site and its offer value.

The findings suggest that landfill sites have a detrimental impact on residential property offer values, with the negative impact increasing with proximity to a landfill site. The negative effect seems to vanish after over 10 km (about 6.21 mi). The developers provide extra facilities including a clubhouse, a children’s play area, a gym and a swimming pool in an effort to mitigate the negative effects of the landfill site on residential properties.

The findings of this study could have implications for property developers, real estate professionals and policymakers in understanding how landfill proximity might impact property offer values.

This study presents many novelties for the Indian housing market: the landfill sites do have a negative effect on the offer value of residential property; the closer the residential property to a landfill site, the higher the negative effect. Further, the developers try and mitigate the negative effect of landfill sites on residential properties by providing additional amenities such as a clubhouse, children’s play park, gym and swimming pool.

Milling is a flexible creation process for the manufacturing of dies and aeronautical parts. While machining thin-walled parts, heat generation during machining essentially affects the accuracy. The workpiece temperature (WT), as well as the responses like material removal rate (MRR) and surface roughness (SR) for input parameters like cutting speed (CS), feed rate (F), depth-of-cut (DOC), step over (SO) and tool diameter (TD), becomes critical for sustaining the accuracy of the thin walls.

Response surface methodology was used to make 46 tests. To convert the multi-character problem into a single-character problem, the weightage was assessed using the entropy approach and the grey relational coefficient (GRC) was determined. To investigate the connection among input parameters and single-objective (GRC), a fuzzy mathematical modelling technique was used. The optimal performance of process parameters was estimated by grey relational entropy grade (GREG)-fuzzy and genetic algorithm (GA) optimization.

SR was found to be a significant process parameter, with CS, feed and DOC, respectively. Similarly, F, DOC and TD were found to be significant process parameters with MRR, respectively, and F, DOC, SO and TD were found to be significant process parameters with WT, respectively. GREG-fuzzy-GA found more suitable for minimizing the WT with the constraint s of SR and MRR and provide maximum desirability of 0.665. The projected and experimental values have a good agreement, with a standard error of 5.85%, and so the responses predicted by the suggested method are better optimized.

The GREG-fuzzy-GA is a new hybrid technique for analysing Inconel625 behaviour during machining in a 2.5D milling process.

The purpose of this paper is to investigate the significant supply chain issues in the small and medium-sized enterprise (SME) food industry. The objectives are to identify the major themes and the dynamic evolution of SME food supply chain (FSC) issues, the current research trends, the different modelling approaches used in SME FSC, and the most addressed SME food sector.

In all, 3,733 published articles from 2002 to 2018 in the Clarivate Analytics Web of Science database were collected, from which 1,091 articles were shortlisted for the review. The authors used bibliographic coupling combined with co-word analysis to identify the historical relations of the research themes that emerged during the periods 2002–2014 and 2002–2018.

This research identified five major research themes such as production and distribution in alternative food networks, relationship, safety and standards in the FSC, greenhouse gas (GHG) emission impact of the farm food system, traceability and product quality in FSC and asymmetric price transmission in the FSC. Among the identified themes, GHG emission impact of the farm food system and traceability and product quality in the FSC have received increasing attention in recent years. The dairy sector is the most addressed sector (36 per cent), followed by fruits and vegetables (27 per cent), meat and poultry (18 per cent), seafood (10 per cent) and grains and oilseed (8 per cent). It is also identified that the dairy sector has received significant attention in the “GHG Emission impact of farm food system” theme. Similarly, meat and poultry sectors have received much attention in the “Traceability and product quality in the food supply chain” theme. Also, the authors identified that the empirical modelling approaches are the most commonly used solution methodology, followed by the conceptual/qualitative methods in the SME FSC.

This study maps and summarizes the existing knowledge base of supply chain issues in the SME food sector. The results of this review provide the major research areas, most commonly used approaches and food sectors addressed. This study also highlights the research gaps and potential future research direction.

The hiring process for information technology (IT) personnel, given its high stakes and intricacies, demands an objective, methodical and nuanced approach. The multifaceted nature of IT roles necessitates a comprehensive evaluation methodology to identify and prioritize pertinent skills and competencies. Therefore, this study aims to devise and implement a robust multi-criteria decision-making model integrating the fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) for assessing and ranking the skills required for IT personnel.

This study integrates expert opinions and insights from the literature to identify five key criteria and 21 sub-criteria essential for IT personnel selection. The fuzzy Analytic Hierarchy Process (AHP) was applied to determine the relative importance of each criterion and sub-criterion. These weighted criteria were then utilized in the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to systematically rank five candidate alternatives based on their suitability for IT roles.

Results identified “Objective of factors” to be the most significant criteria, whereas “Assessment Centre Score” as the most viable sub-criterion. Using the integrated model results of fuzzy AHP-TOPSIS candidate, A3 was the most fitted IT personnel, whereas Candidate A4 has emerged as the poor/ unsuitable fit.

This research helps firms and policymakers use their limited resources efficiently. Furthermore, this study acts as a guideline for future researchers to empirically investigate the impact of listed skills on employee performance.

This study is a pioneering effort in integrating fuzzy AHP and TOPSIS to address the challenges in IT personnel selection, catering to the intricate layers of human evaluative judgments. Its extended applicability and innovative approach makes it a valuable contribution to the existing body of knowledge, serving as a benchmark for future endeavors in related domains.