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The purpose of this paper is to find out the best method for forecasting European Union Allowance (EUA) returns and determine its price determinants. The previous studies in this area have focused on a particular subset of EUA data and do not take care of the multicollinearities. The authors take EUA data from all three phases and the continuous series, adopt the principal component analysis (PCA) to eliminate multicollinearities and fit seven different homoscedastic models for a comprehensive analysis.

PCA is adopted to extract independent factors. Seven different linear regression and auto regressive integrated moving average (ARIMA) models are employed for forecasting EUA returns and isolating their price determinants. The seven models are then compared and the one with minimum (root mean square error is adjudged as the best model.

The best model for forecasting the EUA returns of all three phases is dynamic linear regression with lagged predictors and that for forecasting EUA continuous series is ARIMA errors. The latent factors such as switch to gas (STG) and clean spread (capturing the effects of the clean dark spread, clean spark spread, switching price and natural gas price), National Allocation Plan announcements events, energy variables, German Stock Exchange index and extreme temperature events have been isolated as the price determinants of EUA returns.

The current study contributes to effective carbon management by providing a quantitative framework for analyzing cap-and-trade schemes.

This study differs from earlier studies mainly in three aspects. First, instead of focusing on a particular subset of EUA data, it comprehensively analyses the data of all the three phases of EUA along with the EUA continuous series. Second, it expressly adopts PCA to eliminate multicollinearities, thereby reducing the error variance. Finally, it evaluates both linear and non-linear homoscedastic models incorporating lags of predictor variables to isolate the price determinants of EUA.

This study investigates the various factors which lead to the higher employer brand and studies the relationship of employer branding (EBR) with essential aspects of corporate life, namely, corporate social responsibility (CSR), levels of motivation experienced by employees and the intention to stay (ITS).

The study solicited a research sample from employees working in five sectors, information technology, hospitality, banking and consulting sector (n = 296). The study employed structural equation modeling (SEM) to test the nomological network of EBR.

The study underpins the sub-constructs of EBR. A direct positive and significant relationship was found between EBR and CSR and motivation fully mediated between EBR and ITS.

This study uniquely contributes to the literature by exploring the mediating role of motivation on EBR and ITS's relationship. The study validates the nomological network of EBR by considering its various organizational aspects and the corresponding intertwined relationships.

This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success.

A fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria.

The effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority.

The outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance.

Big data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE.

The purpose of this study is to examine the influence of earnings management during initial public offerings on the listing day returns.

The study collected data for 511 Indian IPOs that came between April 2003 and March 2019 for calculating earnings management. On the basis of the Cross Sectional Modified Jones Model 1995, the paper presents three proxies of earnings management as discretionary accruals (DA), discretionary current accruals (DCA) and discretionary long-term accruals (DLA). The study further used correlation and multiple regression analysis to assess the impact of earnings management on listing day returns.

The findings show that earnings management and listing day returns vary through issue-year and industry-type. Apart from it, the study reveals a greater contribution of short-term accruals in earnings management on the basis of higher DCA values. It also discloses that the aggregate level of earnings management (DA) influences listing returns, whereas DCA and DLA separately have no impact on the listing day returns of the Indian IPOs.

The findings are useful to potential investors and analysts to observe, assess and understand the quality of financial reports that are based on fallacious disclosure of accounting figures. The study also reflects the efficacy of Indian regulatory norms for IPOs in constraining earnings management and underpricing, thus providing meaningful insight to the policy makers and the regulators.

This study is distinguished by its focus on determining the influence of earnings management on listing day returns in Indian IPOs by using three earnings management proxies.

The AirAsia India 2017 (AAI) case presents the situation faced by Tony Fernandes, the CEO of the AirAsia group of companies, in 2017, when he had to respond to the changes in aviation policy made by the Ministry of Civil Aviation (MCA). As per the changes, an airline operating in India could start its international operations without having five years of domestic flying experience provided it deployed 20 of its aircraft or 20% of the total capacity, whichever was higher, for domestic operations. The objective of this case is to help discuss issues relating to sustaining late entry and exploring new growth opportunities in the context of regulatory changes.