Published PapersBharadwaj, Anandhi., Mani, Deepa., Nandkumar, Anand. (Forthcoming) "When Companies Want to Innovate But Investors Won’t Let Them", Harvard Business ReviewRead Abstract >Close >Abstract- When investors value firms for their growth potential rather than current profits – as is the case with startups and tech giants – the companies are not only more likely to invest in digital innovation, but also obtain higher market valuations. In contrast, when investors expect current-period profits – such as from industry incumbents – they are not only less likely to invest in digital innovations, but obtain significantly lower market valuations when they try to become digital leaders. In other words, the market rewards “growth” companies for investment in digital technology, but actively punishes more mature, steadily profitable firms for the same.

Published PapersLanger, Nishtha.,Slaughter, S.A..,Mukhopadhyay, T. (2014) "Project  Managers'  Practical  Intelligence and  Project  Performance in  Software Offshore Outsourcing:  A Field Study", nformation  Systems  Research, 25 (2), 364–384Read Abstract >Close >This study examines the role of project managers’ (PMs’) practical intelligence in the performance of software offshore outsourcing projects. Drawing on the information processing literature, we argue that software offshore outsourcing projects operate in highly uncertain environments, and are prone to severe information asymmetries and challenging management issues. These issues lead to unforeseen critical incidents that must be resolved adequately for the projects to succeed. We posit that PMs can use practical intelligence to effectively address and resolve these critical incidents and the level of PMs’ practical intelligence positively affects performance in these projects. Further, we identify complexity and familiarity attributes of the project, team and PM that affect the information processing needs of the project and exhibit moderating effects on project performance. To evaluate our hypotheses, we analyze longitudinal data collected in an in-depth field study of a leading software vendor organization in India. Our data include project and personnel level archival data on 530 projects completed by 209 PMs. We also employ the critical incidents methodology to assess the practical intelligence of the PMs who led these projects. Our findings indicate that PMs’ practical intelligence has a significant and positive impact on project performance. Further, the benefits from PMs’ practical intelligence are higher for projects that are more complex or that have lower levels of task, team or client familiarity. Our study contributes by conceptualizing and measuring PMs’ practical intelligence and relating it to objective measures of project performance, providing unique empirical evidence of the importance of practical intelligence in a PM, and examining how the effects of PMs’ practical intelligence are moderated by the project context. Given that PMs with higher practical intelligence are scarce resources, our findings also have practical implications for the optimal resource allocation and training of PMs in software offshore services companies.

Published PapersMani, Deepa., Srikanth, Kannan., Bharadwaj, Anandhi. (2014) "Efficacy of R&D Work in Offshore Captive Centers: An Empirical Study of Task Characteristics, Coordination Mechanisms, and Performance", Information Systems Research, 25 (4), 846-864Read Abstract >Close >Abstract Seizing the latest technological advances in distributed work, an increasing number of firms have set up offshore captive centers (CCs) in emerging economies to carry out sophisticated R&D work. We analyze survey data from 132 R&D CCs established by foreign multinational companies in India to understand how firms execute distributed innovative work. Specifically, we examine the performance outcomes of projects using different technology-enabled coordination strategies to manage their interdependencies across multiple locations. We find that modularization of work across locations is largely ineffective when the underlying tasks are less routinized, less analyzable, and less familiar to the CC. Coordination based on information sharing across locations is effective when the CC performs tasks that are less familiar to it. A key contribution of our work is the explication of the task contingencies under which coordination based on modularization versus information sharing yield differential performance outcomes.

Published PapersBapna, Ravi.,Langer, Nishtha.,Mehra, Amit.,Gopal, Ram D.,Gupta, Alok. (2013) "Human Capital Investments and Employee Performance: An Analysis of IT Services Industry", Management Science, 59 (3), 641–658Read Abstract >Close >The rapid pace of technological innovation necessitates Information Technology (IT) services firms to continually invest in replenishing the skills of their key asset base, the human capital. We study the impact of human capital investments in the context of the Indian IT services industry, which has experienced double digit growth rates in the last decade. Indian IT services firms invest significant resources towards training and education of their employees. We examine whether these human capital investments directed towards employee training are effective in improving employee performance and productivity. Our rich employee level panel data set affords us the opportunity to link formal training with performance at the individual employee level. Controlling for unobservable employee characteristics and possible selection bias, we find significant a positive impact of training on employee performance. An additional training course, on an average, helps employees improve their performance by 3.6%. We also investigate the mediating role of employment related characteristics and the type of training on the link between training and performance. We find that employment characteristics such as work experience and whether the employee is a direct hire from an educational institution or a lateral hire from another IT services firm play a significant role in shaping the impact of training on performance. Interestingly, we find it that there is systematic superiority in the high experience laterals’ ability to extract value from firm-provided training. We find significant differences between the impact of specific versus general training and domain versus technical training on performance. We also find that domain and technical training have a substitutive relationship. Taken together, these findings suggest that the value of training is conditional upon a focused curricular approach that emphasizes a structured competency development program. Our findings have both theoretical and practical significance, most important of which is that they justify increased human capital investments to fuel future growth of this important component of the global economy.

Published PapersLin, Mingfeng.,Lucas, Henry.,Shmueli, Galit. (2013) "Too Big to Fail: Large Samples and the p-Value Problem ", Information Systems Research, 24 (4), 906–917Read Abstract >Close >The Internet presents great opportunities for research about information technology, allowing IS researchers to collect very large and rich datasets. It is common to see research papers with tens or even hundreds of thousands of data points, especially when reading about electronic commerce. Large samples are better than smaller samples in that they provide greater statistical power and produce more precise estimates. However, statistical inference using p-values does not scale up to large samples and often leads to erroneous conclusions. We find evidence of an over-reliance on p-values in large sample IS studies in top IS journals and conferences. In this commentary, we focus on interpreting effects of individual independent variables on a dependent variable in regression-type models. We discuss how p-values become deflated with a large sample and illustrate this deflation in analyzing data from over 340,000 digital camera auctions on eBay. The commentary recommends that IS researchers be more conservative in interpreting statistical significance in large sample studies, and instead, interpret results in terms of practical significance. In particular, we suggest that authors of large-sample IS studies report and discuss confidence intervals for independent variables of interest rather than coefficient signs and p-values. We also suggest taking advantage of a large dataset for examining how coefficients and p-values change as sample size increases, and for estimating models on multiple subsamples to further test robustness.

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