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Development of Self Compacting Concrete (SCC) is considered as one of the most significant development in the construction industry due to its numerous inherited benefits. With the introduction of super-plasticizers and viscosity modifying agents, it is now possible to produce concrete with high fluidity, good cohesiveness which does not require external energy for compaction. The proper understanding of the effects of elevated temperatures on the properties of SCC is necessary to ensure the safety of buildings made with SCC during fire. During the present investigation, an attempt has been made to study the stress-strain behaviour of Normal Compacting Concrete (NCC) and Self Compacting Concrete at a temperature of 900°C. A significant reduction in the Ultimate compressive strength of SCC was observed during this study. The reduction was found to be more for SCC compared to Normal compacting concrete. The reduction in the compressive strength of SCC was found to be 81.5 % for M40 concrete when exposed to 900°C.

This chapter examines the factors that may influence the total value created in a joint venture (JV) and also the relative value appropriated by each partner in the venture. We look at the effects of both partners’ embeddedness in prior networks of relationships and the asymmetry of business relatedness of two partners with the JV on these two important outcomes. Results of an event study of stock market reaction to JV announcements by the largest U.S. firms during 1987–1996 suggest that both network embeddedness of partners and the asymmetry of business relatedness of two firms with the JV affect the total value creation of all partners but not the relative value appropriation between the partners.

Creativity and innovation are important for fulfilling organizational goals. In understanding ways to enhance creativity research has moved on from understanding individual creativity, to an organizational analysis of successful firms that encourage creativity. However, an additional source of creativity stems from direct and indirect leadership and the decisions leaders make to enhance creativity throughout the organization. This chapter examines creativity in film and television as influenced by leadership activities at the organization, team, and individual level and describes lessons learned.

Leadership development for strategic impact in high growth export driven organization.

The case is suitable for second and final year students of a two year post graduate management programme (Master's level) on the following courses: leadership – on development of organization wide leadership processes; talent management – for identifying, nurturing and retaining talent in an organization and for developing leadership capabilities in managers; and strategic human resources (HR) – regarding building leadership development and talent management initiatives for creating a strategic level impact in the organization and its joint ventures.

In about 45 years since its inception Anand Automotive Limited (AAL) has established itself as one of the premium firms in auto ancillary manufacturing and export. This case demonstrates how AAL built its leadership development programme. Further, the case elaborates on the coach/coachee mentorship programme at AAL. The case further explores the various initiatives under the broad umbrella of the Anand Leadership Development Programme (ALDP). The ALDP process has been woven into the fabric of HR practices of the organization. AAL sales turnover was USD1.2 billion in 2012 and it has a goal to achieve a turnover of USD2 billion by 2015. Mr K.C. Bhullar, the group head HR, had to plan an HR system which will embed leadership in the tapestry of AAL as an organization. The amalgamation of ALDP in AAL has to be disseminated across all levels at the 19 plants spread across different locations in India. The ALDP is expected to sprout a large number of leaders in AAL who can usher in an extremely quality focused and conscious organization. Such leaders would in their day-to-day demonstration of leadership at AAL help AAL to become an excellent manufacturing organization. This would help AAL to have a leadership position in the global automobile market. ALDP is also expected to create a band of leaders who would help the organization from very senior level strategic management positions and play leadership roles in its joint ventures.

This case can help students to understand how HR practices integrate leadership development programme for the strategic gains of an organization. Students would also understand the role of mentorship in coach/coachee processes.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

This chapter applies arguments advanced by Drnevich and Shanley (this volume) to the strategic leadership literature – an area of work where such multi-level analyses seem likely to be particularly appropriate. In an analysis of the relationship between managerial capabilities and firm performance, this chapter breaks from tradition in the strategic leadership literature by examining the interaction between three levels of analysis. In doing so, this chapter identifies the conditions under which leadership can be a source of competitive advantage for a firm, when labor markets will allocate managerial talent imperfectly across competing firms, and when managers will and will not be able to appropriate the rents their specific managerial talents might generate.

The purpose of the present work is to mathematically model the reliability growth of a multi-version software system that is affected by infected patches.

The work presents a mathematical model that studies the reliability change due to the insertion of an infected patch in multi-version software. Various distribution functions have been considered to highlight the varied aspects of the model. Furthermore, weighted criteria approach has been discussed to facilitate the choice of the model.

The model presented here is able to quantify the effect of an infected patch on multi-version software. The model captures the hike in bug content due to an infected patch.

Multi-version systems have been studied widely, but the role of an infected patch has not been yet explored. The effect of an infected patch has been quantified by modeling the extra bugs generated in the system. This bug count would prove helpful in further studies for optimal resource allocation and testing effort allocation.

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.

In the current market scenario, software upgrades and updates have proved to be very handy in improving the reliability of the software in its operational phase. Software upgrades help in reinventing working software through major changes, like functionality addition, feature enhancement, structural changes, etc. In software updates, minor changes are undertaken which help in improving software performance by fixing bugs and security issues in the current version of the software. Through the current proposal, the authors wish to highlight the economic benefits of the combined use of upgrade and update service. A cost analysis model has been proposed for the same.

The article discusses a cost analysis model highlighting the distinction between launch time and time to end the testing process. The number of bugs which have to be catered in each release has been determined which also consists of the count of latent bugs of previous version. Convolution theory has been utilized to incorporate the joint role of tester and user in bug detection into the model. The cost incurred in debugging process was determined. An optimization model was designed which considers the reliability and budget constraints while minimizing the total debugging cost. This optimization was used to determine the release time and testing stop time.

The proposal is backed by real-life software bug dataset consisting of four releases. The model was able to successfully determine the ideal software release time and the testing stop time. An increased profit is generated by releasing the software earlier and continues testing long after its release.

The work contributes positively to the field by providing an effective optimization model, which was able to determine the economic benefit of the combined use of upgrade and update service. The model can be used by management to determine their timelines and cost that will be incurred depending on their product and available resources.

The purpose of this study/paper is to propose a mathematical model that is able to predict the future popularity based on the view count of a particular YouTube video. Since the emergence of video-sharing sites from early 2005, YouTube has been pioneering in its performance and holds the largest share of internet traffic. YouTube plays a significant role in popularizing information on social network. For all social media sites, viewership is an important and vital component to measure diffusion on a video-sharing site, which is defined in terms of the number of view counts. In the era of social media marketing, companies demand an efficient system that can predict the popularity of video in advance. Diffusion prediction of video can help marketing firms and brand companies to inflate traffic and help the firms in generating revenue.

In the present work, viewership is studied as an important diffusion-affecting parameter pertaining to YouTube videos. Primarily, a mathematical diffusion model is proposed to predict YouTube video diffusion based on the varying situations of viewership. The proposal segregates the total number of viewers into two classes – neoterics viewers, i.e. viewers those viewing a video on a direct basis, and followers, i.e. viewers those watching under the influence.

The approach is supplemented with numerical illustration done on the real YouTube data set. Results prove that the proposed approach contributes significantly to predict viewership of video. The proposed model brings predicted viewership and its classification highly close to the true value.

Thereby, a behavioral rationale for the modeling and quantification is offered in terms of the two varied and yet connected classes of viewers – “neoterics” and “followers.”

YouTube allows its users to upload and view videos on its platform. YouTube provides notification to the subscribers whenever a channel uploads a new video thereby making the channel subscribers the potential viewers of the video. And thus, they are the first to come to know about any new offering. But later on, the view count also increases due to virality, that is, mass sharing of the content by the users on different social media platforms similar to word-of-mouth in the field of marketing. Therefore, the purpose of this paper is to examine different diffusion patterns as they can help to inflate traffic and generate revenue.

YouTube's view count grows majorly through virality. The pattern of view count growth has generally been considered unimodal in most of the available research in the field of YouTube. In the present work, the growth process due to views through the subscribers and views due to word-of-mouth (virality) is presented. Considering that the impact of virality in view count growth comes later in the video life cycle; the viewing patterns of both the segments have been mathematically modeled; independently.

Different models have been proposed to capture the view count growth pattern and how the impact of virality changes the view count growth curve and thereby results in a multimodal curve structure. The proposed models have been verified on various view count data sets of YouTube videos using SPSS (Statistical Package for the Social Sciences), and their ranks have been determined using a weighted criteria–based approach. The results obtained clearly depict the presence of many modes in the life cycle of view counts.

Till now, the literature is evident of the video life cycle following a bell shape curve. This study claims that the initial thrust is by subscribers and then the contribution in the view count by people watching via word-of-mouth comes into picture and brings in another hump in the growth curve.