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Today, a firm’s major concern is to know the way in which an innovation is adopted in the marketplace. The purpose of this paper is to focus on the two-stage nature of diffusion process in which the time lag between people being informed and their act of making final purchase is considered.

The paper discusses an approach based on the time lag for modeling awareness and adoption process as two separate and yet connected processes. Varying forms of time lag (constant, deterministic or random) have been considered while modeling the required framework. Furthermore, an equivalence approach has been shown between the present framework and the two well-known and established approaches of infinite queuing theory and hazard rate function.

The results are verified on sales data of two different consumer durables and it show good prediction capability of proposed models in capturing the real-life scenario. Further, the equivalence approach helps us to quantify such scenarios which were difficult to be modeled with any one particular approach. Further, the possibility of capturing different market scenarios by studying various distribution functions has been identified.

The proposed methodology is based on a two-stage adoption process. The same can be extended to a multi-stage adoption process as in today’s competitive environment. “Motivation” is one such factor that is highly important which can be considered in some later studies. In future, the authors wish to study the multi-stage adoption process considering the different forms of time lag function.

The equivalence approach discussed in the paper can help to cater the possibility of capturing different market scenarios by studying various distribution functions.

The proposed approach helps to cater the time lag between awareness and adoption process and develop different mean value functions to account for the manner in which sales are happening under different circumstances. The proposed methodical approach can also help decision makers in managing their available resources in a prudent manner.

The purpose of this paper is to provide a mathematical framework to optimally allocate resources required for the discovery of vulnerabilities pertaining to different severity risk levels.

Different sets of optimization problems have been formulated and using the concept of dynamic programming approach, sequence of recursive functions has been constructed for the optimal allocation of resources used for discovering vulnerabilities of different severity scores. Mozilla Thunderbird web browser data set has been considered for giving the empirical evaluation by working with vulnerabilities of different severities.

As per the impact associated with a vulnerability, critical and high severity level are required to be patched promptly, and hence, a larger amount of funds have to be allocated for vulnerability discovery. Nevertheless, a low or medium risk vulnerability might also get exploited and thereby their discovery is also crucial for higher severity vulnerabilities. The current framework provides a diversified allocation of funds as per the requirement of a software manager and also aims at improving the discovery of vulnerability significantly.

The finding of this research may enable software managers to adequately assign resources in managing the discovery of vulnerabilities. It may also help in acknowledging the funds required for various bug bounty programs to cater security reporters based on the potential number of vulnerabilities present in software.

Much of the attention has been focused on the vulnerability discovery modeling and the risk associated with the security flaws. But, as far as the authors’ knowledge is concern, there is no such study that incorporates optimal allocation of resources with respect to the vulnerabilities of different severity scores. Hence, the building block of this paper contributes to future research.

This study aims to evaluate the impact of gender diversity on corporate boards on firms’ financial performance in the context of the Indian information and technology (IT) sector. The Companies Act 2013 brought forth mandatory provisions for the appointment of women directors for a certain class of companies. This study explores the case of board gender diversity in the Indian IT sector’s unique setting.

The study uses a fixed effect panel data regression model to achieve its objectives. Two widely used diversity measures, Blau Index and Shannon Index, have been used to enhance the robustness of the results.

The results of the study indicate an insignificant relationship between gender diversity and firms’ financial performance. Even the diversity indices portray insignificant results confirming the outcomes of the study. The study indicates that IT sector firms have not been able to leverage the benefits of board gender diversity.

The results of the study have important policy implications for the government, regulatory bodies and corporates. The outcomes point out that the benefits that could have accrued based on the diversity aspect could not be harnessed, as the women’s representation on corporate boards is extremely low. Policymakers and government shall focus on devising stringent laws so that better representation of women directors can be used for the interests of the firms.

The study is an attempt to fill the gap in the extant literature which has a scarce number of studies conducted in the unique setting of the IT sector (both in developed and developing economies). To the best of the authors’ knowledge, this is the first study on the influence of board gender diversity in the IT sector of a developing economy, backed by socio-cultural reasons.

With growing environmental and social issues worldwide, sustainability disclosures and reporting have become a focal point of discussion. This study aims to investigate the role played by gender diversity in sustainability disclosures in the context of India, over a period of eight years.

The study devises a unique sustainability reporting quality index and employs the generalized ordered logit model, which ensures that results are parsimonious even if the assumptions under a logit model are violated.

The results suggest that with an increase in the percentage of women directors and the number of independent women directors on board, the sustainability reporting quality is likely to improve.

The results of the study shall play a significant role for the corporate houses established in India, as it encourages them to modify their directors' selection process and ensure that women are able to break the “glass ceiling” to reach the upper echelon in the firms.

The study gives an insight into the role played by women directors in sustainability reporting quality aspect, and therefore, the regulatory bodies, as well as policymakers of the Indian economy, shall formulate such regulations which can advance the presence of women on the board and in the decision-making process.

This study is among the first to investigate the relationship between gender diversity and sustainability reporting quality using the generalized ordered logit model which is an improvement over the previously used techniques. Moreover, the unique cultural and institutional setting offered by India, which is an emerging economy, provides a fertile ground for understanding the role of women leaders in the workforce.

This study aims to highlight the impact of introducing women directors to board committees, thereby empowering them to contribute to decision-making, and as a result, influence firms’ financial performance in an emerging economy.

This study uses a fixed-effects panel data regression model to test the impact of gender diversity on corporate boards as well as board committees on firms’ financial performance. Two widely used diversity measures, the Blau index and the Shannon index, have been used to enhance the robustness of the results.

The findings suggest that gender diversity on prominent board committees (remuneration committee and nomination committee) positively affects firms’ financial performance when measured by the market-based performance measure, but it is insignificant when measured through accounting-based performance indicator. Furthermore, the benefits of gender diversity accrue to the firms only when women are part of prominent committees and are engaged in governance mechanisms, rather than just being appointed on corporate boards as a means of tokenism.

This study is among the first to investigate the relationship between gender diversity and financial performance through the lens of committee assignments. Moreover, the unique cultural and institutional setting offered by India, which is an emerging economy, provides a fertile ground for understanding the role of women leaders in the workforce.

Mathematical modeling of innovation diffusion is a constantly evolving field within marketing science. The diffusion process explains the dispersion of an innovation among potential buyers. Prior research on innovation diffusion has been based on modeling varied aspects of real life situations in marketing. One such aspect is studying the adoption process depending on the awareness and motivation level among the customers. Awareness is having knowledge of an innovation, whereas motivation is about the perception of an individual. In line with these aspects, the purpose of this paper is to propose a unified modeling framework for the adoption process based on the awareness and motivation about the product.

When the market is well informed about the product, there are some people who are motivated and some, who have adopted the product earlier and shall now influence others in their buying behavior. It is very much similar to queuing system in which some units are waiting in a queue for the service, service for some units are being processed and some units have already been served. This analogous behavior between two approaches has motivated the use of infinite server queuing theory in modeling adoption of the product. Thereafter, the authors have proposed a unification scheme to model different market scenarios.

From analyzing the values of comparison criteria, it was not clear that which among them is performing best. Thus there was a need for an approach which can judiciously find the optimal model. For this very purpose the authors applied distance-based approach which was capable of computing the optimal model based on the distance of attribute value from the optimal. The analysis performed on two real life sales data sets depict that model in which awareness is following logistic pattern and motivation and adoption are following a constant pattern is ranked one.

The idea has been validated on product. It would be interesting to know how the methodology works on service.

The modeling framework discussed in this paper can be helpful to know from the available set of alternative, which among them is performing better in capturing the spread of the product in the market. The proposed framework offer some managerial guidance by highlighting the unusual aspects of diffusion process and also present an approach to judge the best among a set of different models.