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This study investigates the impact of pre-IPO earnings management on investor demand in the Indian IPO market. It also examines whether earnings management by issuer firms affects IPO valuation, a topic that is underexplored in accounting research.

The study uses the data of 310 IPOs from India during the period 2000–2021. The association between pre-IPO earnings management with investor demand and valuation is tested using cross-sectional ordinary least squares regression models with heteroscedasticity-robust standard errors.

The study finds that the degree of pre-IPO earnings management impacts retail investor demand, measured as their over-subscription multiple. Pre-IPO earnings management is unrelated to institutional investor bidding. Further, this paper suggests no relation between pre-IPO earnings management and IPO valuation.

Future studies could explore various other forms of earnings management and their impact on investor demand and valuation.

The findings of this study will help the investors and regulators to understand the practice of earnings management among IPO firms and how it is related to IPO demand and valuation.

This study contributes to the existing literature on IPO-earnings management and investor demand by documenting that issuer firms engage in earnings management to influence investor demand, particularly retail investor demand. Analysis of IPO valuation reveals that earnings management is mostly unrelated to IPO valuation, contrary to the general perception in the literature.

This study aims to examine earnings management around initial public offerings (IPOs) in India. It also explores the influence of issue characteristics on earnings management around the IPOs.

A sample of 511 IPOs that came during April 2003-March 2019 is studied for calculating earnings management for pre-issue, issue and post-issue years. Using Cross-Sectional Modified Jones Model, the paper presents earnings management on the basis of three proxies i.e. discretionary accruals, discretionary current accruals and discretionary long-term accruals. The influence of issue characteristics on earnings management practised around the IPOs is also observed through correlation and multiple regression analysis.

The paper finds that earnings management is abnormally high during the issue year compared with pre-issue and post-issue years. It also unveils that profitability, premium, age, and size of the issuer significantly determine the level of pre-issue and issue year earnings management practised by Indian IPO issuers.

The findings are useful to stakeholders (potential investors, analysts and regulators) to observe, assess and understand the quality of financial numbers that are based on fallacious disclosure of accounting figures. It provides insight into the possibilities of managed earnings around the issue that could influence investors’ decision-making. Further, the study reflects the efficacy of Indian regulatory norms for IPOs.

To the authors’ knowledge, it is the only Indian study that had used an extensive data set of about two decades to calculate earnings management during pre-issue, issue and post-issue years. The uniqueness of the study further lies in three proxies of earnings management representing short-term and long-term accruals. Moreover, it is the first study to observe the influence of IPO issue characteristics on earnings management.

The purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty.

A novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches.

The proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO₂ emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed.

Unlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design.

This study aims to develop a regression-based machine learning model to predict housing price, determine and interpret factors that contribute to housing prices using different data sets available publicly. The significant determinants that affect housing prices will be first identified by using multinomial logistics regression (MLR) based on the level of relative importance. A comprehensive study is then conducted by using SHapley Additive exPlanations (SHAP) analysis to examine the features that cause the major changes in housing prices.

Predictive analytics is an effective way to deal with uncertainties in process modelling and improve decision-making for housing price prediction. The focus of this paper is two-fold; the authors first apply regression analysis to investigate how well the housing independent variables contribute to the housing price prediction. Two data sets are used for this study, namely, Ames Housing dataset and Melbourne Housing dataset. For both the data sets, random forest regression performs the best by achieving an average R² of 86% for the Ames dataset and 85% for the Melbourne dataset, respectively. Second, multinomial logistic regression is adopted to investigate and identify the factor determinants of housing sales price. For the Ames dataset, the authors find that the top three most significant factor variables to determine the housing price is the general living area, basement size and age of remodelling. As for the Melbourne dataset, properties having more rooms/bathrooms, larger land size and closer distance to central business district (CBD) are higher priced. This is followed by a comprehensive analysis on how these determinants contribute to the predictability of the selected regression model by using explainable SHAP values. These prominent factors can be used to determine the optimal price range of a property which are useful for decision-making for both buyers and sellers.

By using the combination of MLR and SHAP analysis, it is noticeable that general living area, basement size and age of remodelling are the top three most important variables in determining the house’s price in the Ames dataset, while properties with more rooms/bathrooms, larger land area and closer proximity to the CBD or to the South of Melbourne are more expensive in the Melbourne dataset. These important factors can be used to estimate the best price range for a housing property for better decision-making.

A limitation of this study is that the distribution of the housing prices is highly skewed. Although it is normal that the properties’ price is normally cluttered at the lower side and only a few houses are highly price. As mentioned before, MLR can effectively help in evaluating the likelihood ratio of each variable towards these categories. However, housing price is originally continuous, and there is a need to convert the price to categorical type. Nonetheless, the most effective method to categorize the data is still questionable.

The key point of this paper is the use of explainable machine learning approach to identify the prominent factors of housing price determination, which could be used to determine the optimal price range of a property which are useful for decision-making for both the buyers and sellers.

This study aims to examine the reaction of anchor investors (AIs) to pre-IPO earnings management (EM). The authors use the unique detailed bid data from the Indian anchor experiment. The authors also study the reputed AIs’ EM detection ability and pricing behavior in response to pre-IPO EM.

The authors use unique AI bid data for 169 Indian IPO firms. Utilizing the logistic regression and Tobit regression models with industry and year-fixed effects, the authors examine the relationship between various measures of AI participation and proxies of short-term and long-term discretionary accruals.

The authors document that pre-IPO EM is positively associated with the likelihood of anchor backing but negatively related to the likelihood of reputed anchor backing. The findings indicate that AIs are misled by pre-IPO EM, but reputed AIs are not. The authors also observe that reputed AIs, compared to the non-reputed, pay less than the upper band with increasing EM. The findings are robust to using various AI measures and EM proxies.

The findings have significant implications for regulators in the implementation of AI concept in non-anchor markets and better implementation of policies in existing anchor settings. Findings can also be relevant for non-institutional investors in the IPO domain.

This is one of the few studies on institutional investors' IPO bidding behavior in response to pre-IPO EM. However, this is the first study to analyze AIs' IPO bidding behavior in response to pre-IPO EM.

With the function to store individual’s data input, personal health record (PHR) enhanced the accessibility to personal health information. This study aims to assess the factors that impact the intention of Malaysian internet users to use PHR and create a modified technology acceptance model (TAM) for eHealth.

Multivariate statistical analysis was performed on a total of 216 responses using the partial least square technique based on the cross-sectional survey among Malaysian internet users.

Behavioral intention was positively associated to PHR. Subjective norm significantly influenced both attitude and intention to use, whereas trust and perceived usefulness significantly influenced attitude. There was no significant positive impact in the relationships between compatibility and perceived ease of use and intention to use; nevertheless, they positively influenced perceived usefulness. Attitude exhibited mediating influence between trust, perceived usefulness and subjective norm and intention to use. Nonetheless, perceived risk did not affect behavioral intention. Thus, PHR acceptance was well-justified by the modified TAM in evaluating eHealth acceptance.

The eHealth vendors can enhance their marketing and development strategies on related products.

Literatures and empirical evidence on eHealth are still scarce, especially in emerging markets. The role of attitude may not be well-researched in health-care context, therefore was included in this study’s modified TAM. Critical determinants, namely, trust and risk, were added to the model.

This paper examines the relationship between poison pills, real earnings management and initial public offering (IPO) failure.

The authors sampled 2,997 IPO firms that went public during 1993-2015.

The authors find that IPO firms manipulate earnings upward using real earnings management. The authors also find that IPO firms exhibiting a higher level of real earnings management have a higher probability of IPO failure. In addition, the authors find that weak shareholders' governance is positively associated with IPO failure.

These results suggest that poor governance structures in failed firms open the door to manipulating real activities and increasing operational risk.

The study findings are of most significant interest to potential investors and other stakeholders affiliated with a firm going public, an auditor, an underwriter, the lawyers who consult with the firm and employees or executives who might consider joining that firm.

Buying online has become a widespread and common activity for consumers, and, for many organizations, e-commerce has become a very profitable alternative to sell their products and services, also allowing them to leverage their strategy in new geographical markets immediately. Although the literature on the subject is comprehensive, there is a gap in identifying the holistic constructs that are the determinants of consumers' choice of an online store. This research resorts to an exploratory study, based on a nonsystematic literature review, seeking to identify these constructs. The results obtained allowed us to identify the following constructs: consumer behavior, customer experience, web content, catalog, terms and conditions, customer support, perceived value, trust, security and privacy, satisfaction, and loyalty. Customer experience, satisfaction, and loyalty constructs stand out from a strategic perspective.

This purpose of this study is to examine the association between earnings management (accruals earnings management (AEM) and/or real activities manipulation (RAM)) and firm underperformance following seasoned equity offerings (SEOs) using cross-country data.

The study applies ordinary least squares regression analyses to a sample of 11,764 observations on firms from 22 countries over the period from 2005 to 2017. The methods include weighted least squares regression, sub-sampling approach and alternative measures of firm performance, earnings management and legal regime for robustness tests as well as a two-stage least squares instrumental variable (IV) approach to address endogeneity concerns.

The results suggest that RAM has a greater negative impact on post-SEO performance than AEM. The result is economically significant for RAM only. The results also reveal that the negative impact of earnings management, in particular RAM, on post-SEO performance is greater in countries with a strong legal regime than in other countries.

Earnings management around SEOs has important implications for investors, regulators and policymakers. The study suggests that policymakers should improve the current legal conditions to promote fairness in the equity market.

The results from the cross-country data support earlier results from single-country studies on the impact of earnings management on post-SEO performance. The study also provides new evidence on the variation in the impact of earnings management according to the strength of the legal regime operating in a country.