Indian Institute of Management Tiruchirappalli

About: Indian Institute of Management Tiruchirappalli is a education organization based out in Tiruchchirappalli, India. It is known for research contribution in the topics: Emerging markets & Context (language use). The organization has 47 authors who have published 105 publications receiving 1694 citations. The organization is also known as: IIM Trichy.

Teaching the Elective, “Legal Aspects of Innovation and Entrepreneurship” to Management Students

Abstract: Courses on innovation and creativity are being taught at fine arts schools, engineering institutes and management schools among other places. Similarly, courses on entrepreneurship are being taught at engineering institutes as well as at business schools, both at undergraduate and at postgraduate levels. The author has found that the coverage of legal aspects of innovation and entrepreneurship (LAIE) in these courses is either negligently low or completely nonexistent. In this paper, the author demonstrates that the coverage of LAIE is essential to all graduates, which would equip them with necessary skill sets when they decide to become entrepreneurs. The author also demonstrates that such a comprehensive course on LAIE for management students is nonexistent in other Indian Institutes of Management in India and leads the readers toward a conclusion that designing such a course would definitely have takers. Relying on the experience of conducting executive education on this topic, the author is presenting this newly developed 3-credit course on LAIE by discussing its course objectives, its structure, the coverage and the pedagogy. He also shares the experiences and insights from teaching this course at IIM Trichy.

Emergent Heterogeneity in Keyword Valuation in Sponsored Search Markets: A Closer-to-Practice Perspective

Abstract: Reported literature in sponsored search advertising markets asserts that at equilibrium an advertiser has no incentive to swap her position with another advertiser and her bid on a keyword would be bound with the click value acting as an upper bound. We investigate a closer-to-practice case where advertisers do not have an ex-ante known value per click and her bid on a keyword is an outcome of simple cost-cap heuristics on a portfolio of keywords. Using simulations and an experimental setup containing advertisers that have the same upper-cap on cost, we show that the distribution of advertisers’ cost per click and bids are emergent in nature. Keywords exhibit ex post heterogeneity in observed valuation even when all advertisers bid under the same cost-cap constraint. We explore the dynamics of the market, such as temporal stability of advertiser’s bids, and advertiser’s rank based on the click-share along with the distribution of ex post valuation of keywords associated with this closer to practice setup. The results call for a richer understanding of these markets that can incorporate temporal interdependence between auctions of a keyword as well as boundedly rational behavior of advertisers working under imperfect information.

How Content Valence and Online Impression Influence Goal Achievement in Social Media Environment

Abstract: Globally, users are sharing content on social networking sites (SNS) to signal their preferences and to build a positive online reputation. Using the approach and avoidance motivation theory, we ex...

Reinforcement-based heterogeneous ensemble for anomaly detection in streaming environment

Abstract: Intrusion detection in networks is a challenging process, mainly due to huge amount of data and the imbalanced nature of the data. Further, the ever-changing transmission patterns introduce concept drift, which also exhibits a huge challenge. This work presents a heterogeneous ensemble based prediction model to detect anomalies in the network environment. The major goal of the proposed model is to provide faster, more efficient real-time predictions and to enhance the reliability of the model by providing an iterative mechanism to handle concept drifts. The ensemble is created using three varied base learners and the results are aggregated using a voting combiner to provide results. Decision tree, random forest, and gradient boosting trees are used as the base learners. The varied nature of the learners enables effective performances in models. Further reinforcement and an iterative training component is introduced into the model to handle concept drift. Experiments were performed on benchmark intrusion detection data and the results indicate the high performing nature of the model. Comparisons were performed with recent state-of-the-art models in literature and they indicate improved performances of the proposed model, indicating the high performing nature of the proposed ensemble model.

Service Research in Asia: Research Paradigm and Productivity

Abstract: This article traces the journey of service research by analyzing the articles contributed by researchers from Asia based on research productivity and research paradigm. The research analyzes top se...