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The present study aims to examine the short-term effect of disinvestment oriented IPOs and FPOs on the stock market performance of Indian central public sector enterprises (CPSEs), which divested their equity between 2000 and 2017.

The analysis of stock price reaction is conducted for listing dates only in the case of IPOs and three different dates in the case of FPOs through the event study methodology. The three-event dates related to FPOs are public notification date (PND), issue announcement date (IAD) and price band date (PBD).

Overall empirical analysis indicates that investor sentiments are generally insignificant prior to and posts the PND (first date). The second major date of announcement that is (IAD) is new information in the market and returns are found to be significantly negative across both the periods that is before and after IAD. Thus, the analysis depicts strongly negative investor sentiments in the case of IAD. These results are further substantiated by negatively significant CAR (cumulative abnormal returns) values for both the pre and post-event windows of PBD as well.

Empirical analysis concludes that investors do not stand a chance to gain abnormal returns through initiating positions in the stocks of CPSEs during the alternative event dates analyzed.

Since the year 2000, disinvestment through public offering has gathered momentum, and this mode accounts for approximately 62% of the collective disinvestment funds generated by the government of India till now. But there have been very limited research studies on the market performance of disinvested CPSEs. This analysis provides new empirical evidence for the market reactions of retail investors in response to the sale of equity by the Indian government in CPSEs.

This paper aims to semantically linking scientific research events implied by scientific and technical literature to support information analysis and information service applications. Literature research is an important method to acquire scientific and technical information which is important for research, development and innovation of science and technology. It is difficult but urgently required to acquire accurate, timely, rapid, short and comprehensive information from the large-scale and fast-growing literature, especially in the big data era. Existing literature-based information retrieval systems focus on basic data organization, and they are far from meeting the needs of information analytics. It becomes urgent to organize and analyze scientific research events related to scientific and technical literature for forecasting development trend of science and technology.

Scientific literature such as a paper or a patent is represented as a scientific research event, which contains elements including when, where, who, what, how and why. Metadata of literature is used to formulate scientific research events that are implied in introduction and related work sections of literature. Named entities and research objects such as methods, materials and algorithms can be extracted from texts of literature by using text analysis. The authors semantically link scientific research events, entities and objects, and then, they construct the event space for supporting scientific and technical information analysis.

This paper represents scientific literature as events, which are coarse-grained units comparing with entities and relations in current information organizations. Events and semantic relations among them together formulate a semantic link network, which could support event-centric information browsing, search and recommendation.

The proposed model is a theoretical model, and it needs to verify the efficiency in further experimental application research. The evaluation and applications of semantic link network of scientific research events are further research issues.

This paper regards scientific literature as scientific research events and proposes an approach to semantically link events into a network with multiple-typed entities and relations. According to the needs of scientific and technical information analysis, scientific research events are organized into event cubes which are distributed in a three-dimensioned space for easy-to-understand and information visualization.

Risk analysis is a critical investigation field for many sectors and organizations to maintain the information management reliable. Since mining is one of the riskiest sectors for both workers and management, comprehensive risk analysis should be carried out. The purpose of this paper is to explore comprehensively the undesired events that may occur during a particular process with their main reasons and to perform a risk analysis for these events, by developing a risk analysis methodology. For performing risk analysis, discovering and defining the potential accidents and incidents including their root causes are important contributions of the study as distinct from the related literature. The fuzzy approach is used substantially to obtain the important inferences about the hazardous process by identifying the critical risk points in the processes. In the scope of the study, the proposed methodology is applied to an underground chrome mine and obtaining significant findings of mining risky operations is targeted.

Fault tree analysis and fuzzy approach are used for performing the risk analysis. When determining the probability and the consequences of the events which are essential components for the risk analysis, expressions of the heterogeneous expert group are considered by means of the linguistic terms. Fault tree analysis and fuzzy approach present a quiet convenience solution together to specify the possible accidents and incidents in the particular process and determine the values for the basis risk components.

This study primarily presents a methodology for a comprehensive risk analysis. By implementing the proposed methodology to the underground loading and conveying processes of a chrome mine, 28 different undesired events that may occur during the processes are specified. By performing risk analysis for these events, it is established that the employee’s physical constraint while working with the shovel in the fore area, the falling of materials on employees from the chute and the scaling bar injuries are the riskiest undesired events in the underground loading and conveying process of the mine.

The proposed methodology provides a confidential and comprehensive method for risk analysis of the undesired events in a particular process. The capability of fault tree analysis for specifying the undesired events systematically and the applicability of fuzzy approach for converting the experts’ linguistic expressions to the mathematical values provide a significant advantage and convenience for the risk analysis.

The major contribution of this paper is to develop a methodology for the risk analysis of a variety of mining accidents and incidents. The proposed methodology can be applied to many production processes to investigate the dangerous operations comprehensively and find out the efficient management strategies. Before performing the risk analysis, determining the all possible accidents and incidents in the particular process using the fault tree analysis provides the effectiveness and the originality of the study. Also, using the fuzzy logic to find out the consequences of the events with experts’ linguistic expressions provides an efficient method for performing risk analysis.

This study aims to provide an objective analysis of the state-of-the-art and intellectual development of publications related to event study methodology in business research.

The sample includes 1,219 papers related to event study methodology, covering all business disciplines and spanning 34 years from 1983 to 2016.

Through three stages of primary analysis, namely, initial sample, citation and co-citation analyses, the authors identified the publication trends, supplementary techniques, influential publications and intellectual clusters in the area of event study methodology in business.

The findings serve as a benchmark for the extensive literature related to event study methodology in business and may facilitate the transference of the amassed useful techniques among disciplines and the identification of future research directions.

The current study represents as a pioneering effort to review event study-related publications using bibliometric analysis.

To review and address current approaches and limitations to modeling change over time in social entrepreneurship research.

The article provides a narrative review of different practices used to assess change over time. It also shows how different research questions require different methodologies for assessing changes over time. Finally, it presents worked examples for modeling these changes.

Our review suggests that there is a lack of research in social entrepreneurship that takes into account the many different considerations for addressing how time influences outcomes.

This chapter introduces an analytic technique to social entrepreneurship that effectively models changes in predictors and outcomes even when data are non-normal or nested across time or levels of analysis.

This study aims to improve the reliability of emergency safety barriers by using the subjective safety analysis based on evidential reasoning theory in order to develop on a framework for optimizing the reliability of emergency safety barriers.

The emergency event tree analysis is combined with an interval type-2 fuzzy-set and analytic hierarchy process (AHP) method. In order to the quantitative data is not available, this study based on interval type2 fuzzy set theory, trapezoidal fuzzy numbers describe the expert's imprecise uncertainty about the fuzzy failure probability of emergency safety barriers related to the liquefied petroleum gas storage prevent. Fuzzy fault tree analysis and fuzzy ordered weighted average aggregation are used to address uncertainties in emergency safety barrier reliability assessment. In addition, a critical analysis and some corrective actions are suggested to identify weak points in emergency safety barriers. Therefore, a framework decisions are proposed to optimize and improve safety barrier reliability. Decision-making in this framework uses evidential reasoning theory to identify corrective actions that can optimize reliability based on subjective safety analysis.

A real case study of a liquefied petroleum gas storage in Algeria is presented to demonstrate the effectiveness of the proposed methodology. The results show that the proposed methodology provides the possibility to evaluate the values of the fuzzy failure probability of emergency safety barriers. In addition, the fuzzy failure probabilities using the fuzzy type-2 AHP method are the most reliable and accurate. As a result, the improved fault tree analysis can estimate uncertain expert opinion weights, identify and evaluate failure probability values for critical basic event. Therefore, suggestions for corrective measures to reduce the failure probability of the fire-fighting system are provided. The obtained results show that of the ten proposed corrective actions, the corrective action “use of periodic maintenance tests” prioritizes reliability, optimization and improvement of safety procedures.

This study helps to determine the safest and most reliable corrective measures to improve the reliability of safety barriers. In addition, it also helps to protect people inside and outside the company from all kinds of major industrial accidents. Among the limitations of this study is that the cost of corrective actions is not taken into account.

Our contribution is to propose an integrated approach that uses interval type-2 fuzzy sets and AHP method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers. In addition, the integration of fault tree analysis and fuzzy ordered averaging aggregation helps to improve the reliability of the fire-fighting system and optimize the corrective actions that can improve the safety practices in liquefied petroleum gas storage tanks.

The purpose of this paper is to get insights on a real-life portfolio of events from a demand-centred prospective, based on the identification of factors influencing the degree of attendee’s satisfaction, with the ultimate aim of identifying commonalities and prospective cross-leverage strategies among events.

Data from attendees to three major events taking place in Funchal, the capital city of Madeira, were analysed based on multivariate statistics and categorical regression owing to the overwhelming presence of categorical data in the database. The econometric analysis pursued in this paper is based on a sample of 1,830 tourists.

The research demonstrates that the impact of the socio-economic variables and travel arrangements on attendees’ satisfaction is rather irrelevant, irrespective of the event under analysis, with cross-cutting factors defining the overall quality of any event (mainly technical aspects such as aesthetical elements, organization and opening hours) determinant in predicting the overall degree of satisfaction. Another key finding relates to the existence of a number of commonalities among events, in terms of tourists’ profiles, market orientation, themes and resources laying ground to define ready-to-apply cross-leverage strategies.

The analysis concentrated on three major events taking place in the capital city of a peripheral region. The content of the questionnaire co-developed with the destination management organization with order to get access to a large sample of respondents.

Lessons to be learnt in terms of managing a disparate collection of events developed over time in an ad hoc manner. A few examples of cross-leverage strategies are put forward.

The analysis and empirical content portrayed in this study contribute to the literature on event portfolio via description of real-life case examples of how to develop competencies based on post-event analysis in a proactive manner.

Social Media is one of the largest platforms to voluntarily communicate thoughts. With increase in multimedia data on social networking websites, information about human behaviour is increasing. This user-generated data are present on the internet in different modalities including text, images, audio, video, gesture, etc. The purpose of this paper is to consider multiple variables for event detection and analysis including weather data, temporal data, geo-location data, traffic data, weekday’s data, etc.

In this paper, evolution of different approaches have been studied and explored for multivariate event analysis of uncertain social media data.

Based on burst of outbreak information from social media including natural disasters, contagious disease spread, etc. can be controlled. This can be path breaking input for instant emergency management resources. This has received much attention from academic researchers and practitioners to study the latent patterns for event detection from social media signals.

This paper provides useful insights into existing methodologies and recommendations for future attempts in this area of research. An overview of architecture of event analysis and statistical approaches are used to determine the events in social media which need attention.

Given the public availability of secondary data on investments in events such as the Olympics, FIFA World Cup and professional sports, event studies that measure stock market response to these investments have grown. Previous findings are mixed, however, with some studies suggesting that the announcement of sponsorship contracts is a positive event and others finding detrimental effects of the announcement on shareholder value. This study aims to analyze the mixed findings from event studies in sport sponsorship to determine if sponsorship announcements influence stock market response.

The meta-analysis examines more than 20 years of research on event studies in sponsorship (34 studies).

The overall results show a positive, but non-significant effect of partnership deal announcements on shareholder wealth. Further analysis considers the effects of sponsorship announcements by each type of event window to see the impact of the announcement relative to time (pre-announcement, announcement day, post-announcement and pre- to post-announcement). This closer examination of the event window shows that stock prices of sponsoring organizations increased in the pre-announcement window.

Quantitative meta-analytic findings indicate that information about sponsorship deals appears to leak to share markets and positively influence share price. This finding suggests that sponsoring the sports and events found in these event studies is seen as value enhancing for sponsoring firms.