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The purpose of this study is to examine the differential impact of qualitative and quantitative informational signals within the merger and acquisition (M&A) press releases on deal completion and duration. A significant percentage of deals by emerging market acquirers get abandoned before completion, and those that are completed have a longer duration. The limited information about the operations of acquirers from emerging markets creates suspicion among the stakeholders involved in deal resolution, hindering the completion of deals. Thus, using the signal-feedback paradigm, authors investigate how informational signals in the M&A press release impact the deal resolution.

The study employs content analysis on M&A press releases announced by firms from five emerging economies: Brazil, Russia, India, China and South Africa. The technique is applied based on the exploration-exploitation framework developed by March (1991) to categorize the announced deal motives (qualitative information). Next, the authors identify the percentage of relevant quantitative information disclosed in the press release, following which results are obtained using logistic and ordinary least square regressions.

The study reports that deals with declared exploratory motives take longer to complete. Additionally, deals disclosing higher percentage of quantitative disclosure exhibit lower completion rate and increased deal duration.

This is the first study to provide evidence that familiarity bias impacts deal duration as relative to exploitation deals that are familiar to the stakeholders; exploratory deals take longer to conclude. Further, our analysis indicates that a greater percentage of quantitative disclosure may not always reduce information risk but rather be interpreted negatively in the form of the acquirer’s overconfidence in the deal’s potential.

The purpose of this paper is to test whether Indian firms follow the pecking order theory under situations of deficiency as well as surplus.

The study examines Indian firms included in the Bombay Stock Exchange (BSE) 500 index, covering a time span of ten years (2003-2012). An extended model of pecking order theory is tested for deficit and surplus firms separately. The authors use ordinary least square regressions to test the results.

The findings indicate that the pecking order theory is an excellent descriptor for deficit firms, but a poor one for surplus firms. Deficit firms frequently issue debt to fill up deficiency requirements but keep their debt ratios in limit. In marked contrast, surplus firms have low debt to equity ratios and only occasionally redeem debt. They tend to retain funds for future expansion and other operational needs.

The study is limited to firms included in the BSE 500 index, but could be extended to others. Future research work could also focus on debt sub-components.

The present study is useful for firms that are considering capital structure decisions and supports finding that deficit and surplus firms behave differently.

This is the first study separately testing the pecking order between deficit and surplus firms in an emerging market.

The purpose of this paper is to design a sustainable supply chain network (SCN) for omnichannel environment in order to provide better service to customers through flexible distribution. Thus, there is a need to incorporate multiple-channel distribution in the network design of supply chains (SCs).

A multiple-channel distribution supply chain network (MCDSCN) has been proposed under omnichannel environment. This proposed model integrates online giants with local retailer’s distribution network in an uncertain environment with sustainability. To incorporate sustainability, an objective function is added to reduce carbon content along with other objectives of minimization of SC cost. The model turns out to be mixed-integer linear programming model which is coded in GAMS and solved using CPLEX solver.

The proposed MCDSCN model is compared with conventional SCN. Furthermore, it was found that the proposed MCDSCN model has achieved significant saving in SC cost and is also more sustainable than conventional SCN. The proposed model also enables online giants to integrate their distribution network with local retailer’s distribution network.

Through proposed model, customers are free to access product and services as per their choice of channels which increases their convenience, reach and satisfaction.

The proposed MCDSCN model is a novel approach to design flexible distribution systems. This would significantly help organizations to design their distribution network more effectively to meet global competition.

In initial public offerings (IPOs), the media plays a pivotal role by disseminating the information to the investors who generally lack the expertise to understand the information through the prospectus. Thus, media coverage can impact the investment decision of the investors and the IPO performance. Media typically covers the IPO before listing, suggesting that it may play an important role in explaining the opening price rather than the closing price on the day of listing. Therefore, this study aims to disaggregate the traditional IPO underpricing into three categories: voluntary, pre-market and post-market and provides a comparative analysis of the media sentiments impact on the traditional and disaggregated IPO underpricing. The authors’ disaggregated IPO underpricing analysis will facilitate the investors in making an effective investment strategy based on media sentiments.

The study deploys sentiment analysis using bags of n (2) grams approach to gauge the sentiments on 2,891 media articles and uses “robust-regression” technique to analyze them on a sample of 222 Indian IPOs during 2009–2018.

The study reports that the sentiment score is positively related to the traditional underpricing; the sentiment score is positively associated with the pre-market underpricing and does not have any significant relationship with the post-market underpricing; the number of media articles does not play a significant role in explaining the IPO underpricing. The findings highlight the presence of a semi-strong form of efficiency in the Indian IPO market.

Existing literature focuses that the role of media on IPO performance is based on the developed countries. IPO laws differ based on the countries. For instance, in India, investors can check the demand by the other categories of investors on a real-time basis. Thus, it is interesting to study whether, with such a high level of transparency, media can explain IPO performance in the Indian market. Media generally covers IPO before listing; therefore, the present study disaggregates the IPO underpricing to evaluate the role of media on the primary and secondary market separately. It will help the investors to decide when to enter and exit the market.

The purpose of this paper is to ascertain the monthly seasonality in the Indian stock market after taking into consideration the market features of leptokurtosis, volatility clustering and the leverage effect.

Augmented Dickey-Fuller, Phillips-Perron and Kwaitkowski-Phillips-Schmidt-Shin tests are deployed to check stationarity of the series. Autocorrelation function, partial autocorrelation function and Ljung-Box statistics are employed to check the applicability of volatility models. An exponential generalized auto regressive conditionally heteroskedastic model is deployed to test the seasonality, where the conditional mean equation is a switching model with dummy variables for each month of the year.

Though the financial year in India stretches from April to March, the stock market exhibits a November effect (returns in November are the highest). Cultural factors, misattribution bias and liquidity hypothesis seem to explain the phenomenon.

The paper endeavors to provide a review of possible explanations behind month-of-the-year effect documented in literature in the past four decades. Further, the unique evidence from the Indian stock market supports the argument in the literature that monthly seasonality, by nature, may not be a consistent/robust phenomenon. Therefore, it needs to be examined from time to time.

As the seasonality in the stock market and resultant anomalies are dynamic phenomena, the paper reports the current seasonality/anomalies prevalent in the Indian market. This would aid investors in designing short-term investment portfolios (based on anomalies present) in order to earn abnormal returns.

Big data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for organisations to handle volatile, dynamic and global value networks. This paper aims to investigate the mediating role of “big data analytics” between Supply Chain 4.0 business performance and nine performance factors.

A two-stage hybrid model of statistical analysis and artificial neural network analysis is used for analysing the data. Data gathered from 321 responses from 40 Indian manufacturing organisations are collected for the analysis.

Statistical analysis results show that performance factors of organisational and top management, sustainable procurement and sourcing, environmental, information and product delivery, operational, technical and knowledge, and collaborative planning have a significant effect on big data adoption. Furthermore, the results were given to the artificial neural network model as input and results show “information and product delivery” and “sustainable procurement and sourcing” as the two most vital predictors of big data adoption.

This study confirms the mediating role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. This study guides to formulate management policies and organisation vision about big data analytics.

For the first time, the impact of big data on Supply Chain 4.0 is discussed in the context of Indian manufacturing organisations. The proposed hybrid model intends to evaluate the mediating role of big data analytics to enhance Supply Chain 4.0 business performance.

Portfolio optimization is the process of making an investment decision on a set of assets to realize high returns with low risk. It has three major stages: asset selection, asset weighting and asset management. Asset selection is an important phase because it influences asset allocation and ultimately affects the returns of a portfolio. Today, there is an increase in the number of listings on a stock exchange. Therefore, it is important for an investor to screen and select stocks for investment. This study focuses on the first stage of the portfolio optimization problem, namely, asset selection. The purpose of this study is to evaluate and select profitable stocks quoted on National Stock Exchange (NSE) for portfolio optimization.

Financial ratios are considered as the input and output parameters for evaluating the financial performance of the firms. This study adopts a hybrid principal component analysis (PCA) and data envelopment analysis (DEA) approach to evaluate the efficiency of the firms. Based on the efficiency scores, the firms are selected for the investment process.

The model helps to determine the relative efficiencies of the firms. The efficient firms are considered to be the potential stocks for investment. It helps the investors to screen the stocks from a large number of stocks quoted on NSE.

One of the limitations of the standard DEA model is that it fails to discriminate the firms when the number of input and output parameters are larger than the number of firms. To overcome this problem, either a parameter can be ignored or weight-restricted DEA can be applied. When an input/output parameter is dropped, the information in that variable is lost. Weight-restricted DEA model uses expert opinion for measuring the relative importance of input and output parameters. Expert opinion is subjective and might be biased. The PCA-DEA model helps to identify the efficient firms by improving the discriminatory power of standard DEA without any loss of information and without the need for expert opinion, which might be biased.

Asset selection is an important stage in the investment process. Selection of stocks based on the efficiency score is an easier option available to the investors. But the misclassification of firms either due to biased expert opinion or discrimination inability of DEA can be costly to an investor. The PCA-DEA model overcomes both these limitations. Investors can select the potential candidates for asset allocation based on the efficiency scores obtained using the PCA-DEA model. Further, the relative efficiencies obtained can help the firms to benchmark their performance against the best performing firms within their industry.

This paper is one of few papers to adopt the PCA-DEA framework to select stocks in the Indian stock market.

This study initially aims to identify the barriers to the big data analytics (BDA) initiative and further evaluates the barriers for knowing their interrelations and priority in improving the performance of manufacturing firms.

A total of 15 barriers to BDA adoption were identified through literature review and expert opinions. Data were collected from three types of industries: automotive, machine tools and electronics manufacturers in India. The grey-decision-making trial and evaluation laboratory (DEMATEL) method was employed to explore the cause–effect relationship amongst barriers. Further, the barrier's influences were outranked and cross-validated through analytic network process (ANP).

The results showed that “lack of data storage facility”, “lack of IT infrastructure”, “lack of organisational strategy” and “uncertain about benefits and long terms usage” were most common barriers to adopt BDA practices in all three industries.

The findings of the study can assist service providers, industrial managers and government organisations in understanding the barriers and subsequently evaluating interrelationships and ranks of barriers in the successful adoption of BDA in a manufacturing organisation context.

The paper is one of the initial efforts in evaluating the barriers to BDA in improving the performance of manufacturing firms in India.

Small and medium enterprises (SMEs) play a crucial role in national economies worldwide, generating employment and contributing to innovation. This study tries to investigate the performance of the newly started IPO platform for the SMEs in India through a two-staged framework developed to measure pre-market and post-market underpricing separately and the impact of economic policy uncertainty (EPU) on the IPO returns using the EPU index which is based on newspaper coverage frequency. Further, the long-run performance of SME IPOs and the factors affecting the same have also been analyzed. The two-staged framework is helpful in capturing the impact of different factors separately on the two distinctive markets and providing effective investment strategies to the investors.

A sample of 384 SME IPOs issued during 2012–2018 has been analyzed using robust regression analysis.

The study highlights the fact that there are differences in the factors affecting pre-market and post-market underpricing and reports that investors subscription rate, issue expenses, lead manager reputation and EPU are positively associated, whereas the age of the firm is negatively associated with the pre-market underpricing, and lead manager reputation positively impacts the post-market underpricing whereas issue premium and pre-market underpricing are negatively associated. Pre-market underpricing subsumes all the impact of EPU (publicly available information) in it, hence providing credence to the semi-strong market hypothesis of the Efficient Market Hypothesis (EMH). The long-run performance of SME IPOs increases with time, and lead manager reputation, pre-market and post-market underpricing are positively related to the one-year return whereas issue size, turnover and issue expense are negatively related.

This paper is believed to be the first attempt to analyze the performance of SME IPOs by disaggregating IPO underpricing. The findings of this study will have a great insight for the investors and policymakers.

This paper aims to assess the impact of corporate social irresponsibility (CSiR) media coverage on firm performance in India. It also analyses the effects of the environment, social, governance, and cross-cutting issues on firm performance.

The paper utilizes a sample of Indian firms from the Reprisk^® database, amounting to 1,103 CSiR media coverage counts for 693 firm-year annual observations from 2008 to 2015. Further, Reprisk^® segregates comprehensive CSiR coverage counts into the environment, social, governance and cross-cutting issues, for which the study runs the fixed effects panel regression. The study takes year-fixed effects, industry-fixed effects and clustered standard errors at the industry level.

The results of this study indicate that CSiR coverage negatively influences the firm performance of Indian firms. All issues, including social, governance and cross-cutting, except environmental issues, negatively impact firm value in India.

The involvement of firms in CSiR costs the firms financially and drives down firm performance. Social issues, including community and employee-related matters, governance issues and cross-cutting issues, also reduce the firm performance.

The insignificant environmental impact on firm performance does not indicate that environmental issues have no detrimental consequences. Instead, it might need more stakeholders' awareness to understand the harmful implications of environmental issues on society.

Limited studies have explored CSiR in India so far. The study is novel as it analyses the Reprisk^® database and its segregation of media counts into the environment, social, governance and cross-cutting issues in the Indian context.