Gross profit manipulation in emerging economies: evidence from India

Purpose This study aims to explore peer performance as the motivation behind gross profit manipulation through two different channels, namely, cost of goods sold (COGS) misclassification and revenue misclassification. Design/methodology/approach Gross profit expectation model (Poonawala and Nagar, 2019) and operating revenue expectation model (Malikov et al., 2018) are used to measure COGS and revenue misclassification, respectively. The panel data regression models are used to analyze the data for this study. Findings The study results show that firms engage in gross profit manipulation to meet the industry’s average gross margin, implying that peer performance is an important benchmark that firms strive to achieve through misclassification strategies. Further results exhibit that firms prefer COGS misclassification over revenue misclassification for manipulating gross profit, implying that firms choose the shifting strategy based on the relative advantage of each shifting tool. Practical implications The findings suggest that firms that just meet or slightly beat industry-average profitability levels are highly likely to engage in classification shifting (CS). Thus, investors and analysts should be careful when evaluating such firms by comparing them with other firms in the same industry. Originality/value First, this study is among earlier attempts to investigate CS motivated by peer performance. Second, this study investigates both tools of gross profit manipulation by taking a uniform sample of firms over the same period and provides compelling evidence that firms prefer one shifting tool over another depending on the relative advantage of each shifting tool.

Pictures Library of Smoking Cravings: Development and Verification of Smokers and Non-smokers

Background: The cue-induced craving by addiction related materials is commonly employed in addiction research; however, no existing standardized picture database based on the expectation model of craving has been developed. We prepared and validated a Pictures Library of Smoking Cravings (PLSC) in this study.Methods: We captured pictures 366 smoking and 406 control pictures (matched in content). We selected 109 smoking pictures and 115 control pictures and asked participants to provide ratings of craving, familiarity, valence, and arousal induced in them. Participants were divided into three groups: non-smokers (n = 211), light smokers (n = 504), and heavy smokers (n = 101).Results: The results showed that smoking pictures evoked a greater craving, familiarity, and arousal than control pictures in smokers (ps < 0.01). In addition, craving caused by smoking pictures was positively associated with the Fagerström test for nicotine dependence score in dependent smokers.Conclusions: Overall, the contemporary results showed that PLSC is effective and can be used in smoking-related studies.

Predictors of Coping with Expectation Violation: An Integrative Review

The present review investigates factors that predict three processes that lead to persistence versus change of expectations after confrontation with expectation violations, based on the ViolEx (Violated Expectation) Model (Gollwitzer, Thorwart, & Meissner, 2018) and related models. We address four groups of predictors: a) characteristics of the expectation, b) characteristics of the expectation-violating event(s), c) broader situational characteristics, and d) personality characteristics. The bulk of studies conducted in this area looked at expectation change in the direction of the experienced violation (accommodation) as their central dependent variable. The strongest empirical support was found for accommodation being less likely and minimizing of the potential impact of the discrepant information (immunization) being more likely to occur a) after the reality turns out to be worse rather than better than expected, b) if disconfirming events are more ambiguous, and c) if depressed rather than healthy people are confronted with better-than-expected events. Given the high heterogeneity between studies on assessed predictors, we recommend a more comprehensive and unifying approach that tests the relative impact and the interplay of the whole range of predictors across paradigms.

Forecasting Approach for Water Pollution Based on Markov Forecast Model

The time scale-arrangement gauging of water contamination is significant and troublesome issue of water contamination organize framework. The time-arrangement information of water contamination is tremendous, high-dimension and non-linear, data removal of it is troublesome. To understand the information digging and gauging for arrangement information of water contamination effectively, an enhanced expectation replica dependent on the smallest amount squares bolster vector engine is introduced in this magazine. To lessen the element of tests, the bit head segment investigation technique is utilized to remove the element data, which contains the vital parts of tests. At that point applying the unascertained thorough assessment technique and Markov estimate to the water value assessment, it defeats not just the vulnerability and arbitrariness in the water quality framework, yet in addition the irregularity of weight assurance, since it utilizes the credit progressive strategy to decide weight of each contamination factor impartially. At that point, through the checking information computation shows the gauge model is exact, yet in addition the outcome is logical and sensible, in view of the use of un-ascertained science strategy. The request model shows that this representation acquires acceptable outcomes, and gives a method of water superiorityestimate. At last, the proposed expectation model is applied in water contamination time-arrangement information determining tests. The exploratory outcomes show that the proposed approach has some preferred exhibitions over the overall techniques, for example, the great prescient precision and solidness in the time-arrangement estimating of water contamination.

Optimistic Prediction Model for the COVID-19 Coronavirus Pandemic Based on the Reported Data Analysis

The world is facing new challenges every day; however, with the spread of the pandemic around the world, this new challenge is different. The pandemic is increasing and concentrating various challenges simultaneously. Although different sectors are facing consequences, the most important sectors, that is, health and economy are the most affected. When the pandemic began, it was not known how long it would last, which complicated health and economic planning. Therefore, it is important for decision makers and the public to know the predictions and expectations of the future of these challenges. In this work, the current situation is analyzed. Then, an expectation model is developed based on the statistics of the pandemic using a growth rate model based on an exponential and logarithmic rate of increase. Based on the available open data about the pandemic spread, the model can successfully predict future expectations, including the duration and maximum number of cases of the pandemic. The model uses the equilibrium point as the day the cases decrease. The model can be used for planning and the development of strategies to overcome these challenges.

Optimistic Prediction Model for the COVID-19 Coronavirus Pandemic Based on the Reported Data Analysis

The world is facing new challenges every day. however, with the pandemic spread over the world, a new challenge is different. The pandemic challenge is taking all the challenges to concentrate and increase in one time. Although different sectors are facing consequences, the most important sectors, health and economy are the most affected. When the pandemic start, it is not known when it will last for different health and economic planning. Therefore, it is very important for decision makers and the public to know the prediction and expectations of the future of these challenges to successfully goes over it. In this work, the current situation is analyzed. Then, an expectation model is developed based on the statistics of the pandemic using a growth rate model based on the exponential and logarithmic increase rate. Based on the available open data about the pandemic spread, the model successfully can predict the future expectations. The model expects the time and the maximum number of cases of the pandemic. The model uses the equilibrium point as the day the of cases decreases. The model can be used for planning and development of strategies to overcome the challenges.