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Research on gender and finance finds that women chief executive officers (CEOs) are relatively risk-averse and more ethical than their male counterparts. These differences are often presented as reasons for lower earnings management by firms led by women. A strand of contrasting literature however finds the notions of women being risk-averse and ethical not necessarily true for women occupying top leadership positions as women successful in shattering the glass ceiling adopt behaviors like men. This study attempts to understand the differences between the ethical tendencies of the two genders by examining if CEO power impacts the relation between CEO gender and earnings management.

The authors begin the analysis using standard regressions using the propensity score matched (PSM) samples and examine if CEO power mediates or amplifies relationship between CEO gender and earnings management. The authors use ordinary least squares (OLS) regression approach and instrumental variables (IV) estimation to address the endogeneity concerns.

This study’s results suggest that the relationship between CEO gender and earnings management is mediated by CEO power. The authors find that women CEOs with lower power engage in lower earnings management. However, women CEOs with more power tend to engage in greater levels of earnings management than their male counterparts.

This study contributes the finance literature by showing women leaders successful in occupying top leadership positions are not necessarily more risk averse and more ethical. Less powerful women CEOs are subjected to potentially higher levels of scrutiny and are forced into an environment where they have to be seen as ethical. However, powerful women face the same concerns as their male counterparts and not necessarily more ethical.

This study aims to explore information asymmetry (IA) (as measured by the adverse selection component of the bid-ask spread) around S&P 500 revisions.

The authors use adverse selection cost of trading measures to examine the effects of S&P 500 index composition changes on the trading environment from 2001 to 2010.

The authors find that the adverse selection cost of trading significantly decreases post-addition and increases post-deletion. However, the intraday price dynamics of additions to the index seem to be distinct from those of deletions from the index. The event period cumulative abnormal returns (CARs) for additions are significantly associated with the change in the adverse selection cost of trading. However, this association is non-significant for deletions. The CARs for deletion events are found to be significantly associated with the change in realized spreads. Realized spreads are a measure of revenue earned by liquidity providers in the market.

This study helps better understand the dynamics of two types of IA – one from a firm to investor and the other between investors – and presents evidence of the role of adverse selection in index changes. By doing so, it helps better understand the mechanism driving price formation post-addition to and deletion from an index.

The contemporary zombie genre is known for exploring what the end will look like, with its widespread infection, chaos and violence – all images that resonate in a post-9/11 America. These zombie narratives also speak to a present-day America with their emphasis on diminishing individuality and agency. Unlike early Haitian incarnations of the zombie figure, the modern zombie terrifies because no singular agent possesses the victim’s mind. In contrast, the light-hearted CW television show, iZombie (2015–) rethinks the zombie paradigm. Not only does it envision how zombies would manifest in everyday life, without the requisite apocalypse, but it also subverts the antiquated gender politics common to the genre by providing viewers with a female zombie protagonist, Olivia Moore (Rose McIver) who is not only highly functional, but also female and with plenty of agency. Moore, through whose eyes the show is told, absorbs personality traits and memories belonging to the brains she eats, from frat boy to alcoholic, stripper to housewife. This device creates such a cornucopia of roles for McIver to explore that it brings to mind the work of American photographer Cindy Sherman, providing a rare multi-dimensional woman on TV. iZombie also takes the contemporary zombie text’s reliance on the trop of infection one step further. This chapter not only examines iZombie’s unusual female point of view, but also its portrayal of ‘zombie-ness’ as a chronic contagious illness with many similarities to HIV.

Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy. Past studies have focused on the outcome of failures, while, there is a dearth of studies focusing on ongoing firms in bad shape. We plug this gap and attempt to identify underlying communication patterns for firms witnessing prolonged underperformance. Using text mining, we extract and analyze semantic, linguistic, emotional, and sentiment-based features in non-numeric communication channels of these poor-performing firms and their peers. These uncovered patterns highlight the use of vocabulary and tone of communication, in correspondence to their financial well-being. Furthermore, using such patterns, we deploy various Machine Learning algorithms to identify loser firm(s) way ahead in time. We observe promising accuracy over a time window of five years. Such early warning signals can be of critical importance to various stakeholders of a firm. Exploration of writing style-related features for any firm would help its investors, lending agencies to assess the likelihood of future underperformance. Firm management can use them to take suitable precautionary measures and preempt the future possibility of distress. While investors and lenders can be benefitted from this incremental information to identify the likelihood of future failures.

The learning outcomes are as follows: introduce the concept of the decision-making process, decision-making unit and hierarchy of effects and marketing strategy; identify the critical aspect of segmentation, targeting and positioning; and highlight the critical element of pricing and communication media.

In early January 2017, Mr Ashish and Mr Rahul, co-founders of Biziga, a company engaged in training through simulation for management education, was at crossroads. Keeping in view the challenges of the emerging Indian market, Biziga envisioned creating participant-centric business learning simulations. The initial responses and feedback received from several top B-schools were promising. However, the euphoria did not last long. Biziga retained only a few of its initial clients from the Tier-1 B-schools who had adopted the product. But the response received from other categories of B-schools was not very encouraging. Acquiring new clients from these institutes was the major challenge. The founders of Biziga had differences in their thought about the strategic path they should pursue to achieve future growth. There were several options to achieve the goal of a target revenue of INR 1bn in the next five years and be known as a virtual gamification company with a complete bundle of business simulation products. They had to finalize for the financial year 2017-18 the most feasible and promising option/s that would have a long-term impact on the company’s future growth and success in the upcoming meeting scheduled in the last week of February 2017.

Postgraduate students and executive students.

Teaching notes are available for educators only.

CSS 8: Marketing.

The purpose of this study is to present a comprehensive review of the fundamental concepts and terminologies pertaining to different types of aluminium metal matrix composites, their joining techniques and challenges, friction stir welding (FSW) process, post-welding characterizations and basic control theory of FSW, followed by the discussions on the research reports in these areas.

Joining of aluminium metal matrix composites (Al-MMC) poses many challenges. These materials have their demanding applications in versatile domains, and hence it is essential to understand their weldability and material characteristics. FSW is a feasible choice for joining of Al-MMC over the fusion welding because of the formation of narrow heat affected zone and minimizing the formation of intermetallic compounds at weld interface. The goal in FSW is to generate enough thermal energy by friction between the workpiece and rotating tool. Heat energy is generated by mechanical interaction because of the difference in velocity between the workpiece and rotating tool. In the present work, a detailed survey is done on the above topics and an organised conceptual context is presented. A complete discussion on significance of FSW process parameters, control schemes, parameter optimization and weld quality monitoring are presented, along with the analysis on relation between the interdependent parameters.

Results from the study present the research gaps in the FSW studies for joining of the aluminium-based metal matrix composites, and they highlight further scope of studies pertaining to this domain.

It is observed that the survey done on FSW of Al-MMCs and their control theory give an insight into the fundamental concepts pertaining to this research area to enhance interdisciplinary technology exploration.

This paper aims to test the extent to which downward bias due to a floating-point exception in probability of informed trading (PIN) estimates obtained using the Easley, Hvidkjaer and O’Hara (EHO; 2002) method is remedied using the Yan and Zhang (YZ; 2012) method. The paper also aims to test the sample-size sensitivity of EHO PIN and identify PIN determinants for acquirers and targets in the biotech sector.

EHO and YZ PIN performances are compared for US biotech acquirers and targets around their mergers and acquisition (M&A) announcements. The sampling method of Kryzanowski and Lazrak (2007) is used to assess sample-size sensitivity of announcement window EHO PIN estimates. Cross-sectional regressions are estimated to identify PIN determinants.

EHO and YZ PIN are not significantly different. EHO PIN exhibits significant sample-size sensitivity. Information leakage prior to M&A announcements is strongly affected by some firm characteristics. Significant determinants of PIN behavior around M&A announcements include insider and institutional holdings and research and development (R&D) expense.

Findings imply that PIN partially reflects the activities of insiders and other informed investors about takeover intentions. Subsequent research can examine PIN behavior around pre-announcement rumors for M&As in the same or other industries and for potential targets that are peers of the M&A targets.

This paper contributes to the ongoing debate in the empirical finance literature on whether PIN measures informed trading by examining its behavior and the importance of some methodological issues associated with its use in examining market behavior around M&A announcements.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

The purpose of this paper is to explore the key performance indicators (PIs) that serve as a decision support tool in case of dairy supply chain practices and to analyze their interactions in the context of Indian dairy industry sector. A total of 11 PIs have been identified through the literature review and the opinions of an expert team consisting of managerial and technical experts from dairy industry and academics.

A solution methodology based on the interpretive structure modeling (ISM) technique is used to analyze the interactions among PIs and to propose a structural model. The developed model not only helps in understanding the contextual relationship among the PIs, but also in determining their interdependence to assess the supply chain performance in dairy industry. Further, the importance of PIs has been determined based on their driving and dependence power by using MICMAC analysis.

The ISM-based model suggests four PIs at first level, three PIs at second level, one PI at third level as well as one PI at fourth level and two PIs at fifth level. Model allocates to the effective information technology, brand management, responsiveness in shipment and accuracy and a control over wastages as the key PIs in the dairy industry sector. The effective traceability systems, cold chain infrastructure, quality management and the support for technological innovations are the next major PIs. There exists no autonomous PI in MICMAC analysis which proves the importance of identified PIs in the case study.

The proposed model is an attempt to capture the dynamics of milk processing sector and to incorporate all relevant constraints related to internal and external environments that would significantly improve the supply chain performance in the dairy industry.

The model developed in this study has been tested in the cooperative milk processing units based in India and also discussed with the experts from academics. This work may help practitioners, regulators and dairy industry professionals to focus their efforts toward achieving high performance by the effective implementation of the identified PIs.

In this study, 11 PIs are considered. Interactions among PIs are evaluated with the help of the ISM matrix. Out of the 11 PIs, six demonstrate both strong driving and dependence power as explained in the MICMAC analysis.

The paper aims to review existing performance indicators in technology business incubators (TBIs) and propose some new indicators with a focus on incubation activities in higher educational institutes (HEIs) of India.

Performance indicators of various types of incubators were identified from research papers followed by interview, consultation and suggestion from experts of the subject. Nature of interrelationship between the identified indicators has been established with the help of Interpretive Structural Modelling methodology and Matrice d’impacts croisés multiplication appliquée á un classment analysis.

Number of ideas came for screening and number of ideas converted to start-ups, survival rate of incubatees is the indicators which have the highest driving power followed by time taken in screening an idea and number of failed or rejected ideas returned back into incubation. Few indicators (driving indicators) are affecting performance of other indicators as well.

Some performance indicators are proposed which can be used for measuring performance of technology incubators in India. The actual implications will be known when these findings are used to assess performance of some technology incubator. This also is the limitation of the study that some cases can be included to validate the findings of this research.

A total of 15 performance indicators for measuring performance of TBIs in Indian HEIs have been proposed. The proposed indicators will help incubator management to prioritize the efforts and resource allocation.

TBIs are looked upon as mechanism for promoting entrepreneurial culture in Indian HEIs. Their success is well linked to growth of society. This research will help technology incubators to identify the most important factors in incubation process. Performance improvement will directly affect society in whole. Culture of IEE (Innovation, Entrepreneurship and Employment ) can be achieved through technology incubators

Identification of new indicators for performance measurement of incubators in Indian HEIs is the novelty of this research. This has a lot of value due to multilevel hierarchy model.