Onward and upward? An empirical investigation of gender and promotions in Information Technology Services

The shaky ascent of women up the organizational ladder is a critical factor that may contribute to the lack of women in information technology (IT). In this study, we examine the effect of gender on the likelihood of employee promotions. We further examine whether women get an equal lift in promotion likelihood from performance improvements, work experience, and training as men. We analyze archival promotion data, as well as demographic, human capital, and administrative data for 7,004 employees at a leading IT services firm located in India for the years 2002–2007 and for multiple levels of promotion. We develop robust econometric models that consider employee heterogeneity to identify the differential effect of gender and performance on promotions. We find that, contrary to expectations, women are more likely to be promoted, on average. However, looking deeper into the heterogeneous main effects using hierarchical Bayesian modeling reveals more nuanced insights. We find that, ceteris paribus, women realize less benefit from performance gains than men, less benefit from tenure within the focal firm, but more benefit from training than men. These results suggest that despite the disparity in returns to performance and experience improvements, women can rely on signaling mechanisms such as training to restore parity in promotions. We find that the effects of gender and performance vary with the level of employee promotion; although not as much as men, women benefit more from performance gains at higher organizational levels. Our findings suggest several actionable managerial insights that can potentially make IT firms more inclusive and attractive to women

Too Risky to Bid? Women in OLMs and STEM Competitive Environments

Online labor markets (OLM) are increasingly common sources for identifying trained individuals for technological work. Yet, like much of the tech industry, OLMs suffer from under-representation of women. We examine why women may choose not to participate in bidding for software development and analytics projects on OLMs. We theorize that pertinent project factors – project complexity and overall project competition – increase the risk profile of such work and disproportionately dissuade women from bidding for these projects, relative to men. We test these hypotheses using experiments conducted on Amazon Mechanical Turk (AMT). Comparing the effect of higher project complexity, greater boundary spanning requirements, and higher competition on the propensity to bid for riskier projects for women versus men, and on the bid amount issued when they do bid, we find that women are indeed deterred by project complexity in their bid decision (and to bid lower amounts), but are more likely to bid for projects with higher boundary spanning requirements or more competition. We contribute to the IS literature by establishing the specific factors affecting women’s participation and wages in OLMs and suggest several actionable managerial insights to make OLMs more inclusive and attractive to women in IT

Project Managers\u27 Skills and Project Success in IT Outsourcing

What skills do project managers (PMs) need, and how do these skills impact project success in IT outsourcing? In this study, we seek to identify what factors impact IT project outcomes, such as costs and client satisfaction, given the project characteristics and PM’s hard and soft skills. We examine data collected from a field study conducted at a major IT service provider in India. Our results suggest that while hard skills such as technical or domain expertise may be essential in a PM, soft skills, such as tacit knowledge of organizational culture and clients, are more important for project success. The results are robust to different specifications

Inclusion is not a slam dunk: A study of differential leadership outcomes in the absence of a glass cliff

© 2019 Elsevier Inc. Racial bias continues to act as one of the most thought provoking and controversial topics in our society. Even as organizations implement steps and policies to minimize discriminatory practices, evidence of bias in organizational decision-making persists. While much research has been devoted to the study of racial bias in hiring and promotion decisions, this study focuses on the effect of biases on employment outcomes of minority leaders after they have been hired or promoted to leadership positions that are comparable in quality to those of their white peers (i.e. no glass cliff present). More specifically, we investigate how discrimination influences performance rewards and employment separation decisions pertaining to minority leaders. The study uses archival data from the National Basketball Association collected from the year 2003 to 2015. From this data set, we utilize measures of head coaches\u27 objective performance, reward allocation, and their likelihood of employment separation to find limited support for the hypotheses that minority leaders are given less time in position to achieve success and that when they do achieve success, they may be less likely than white leaders to be recognized for their accomplishments. Our findings suggest that in addition to researching selection processes, understanding why racial minorities are underrepresented in leadership positions also requires insight into the employment outcomes experienced by minority leaders

Activating the Sisterhood: A Structural and Temporal Analysis of Sustained Connective Action in #MeTooIndia

We study the important issue of temporality in social media-enabled new forms of collective engagement within the context of #MeTooIndia movement. Using pertinent, publicly available Twitter data over two time periods, we examine how sustained connected action and social movements evolve over time. We find that users are more likely to use personal action frames for connective action at the beginning of the social movement, but over time, these action frames coalesce into more collective action frames. We also find a high degree of clustering and mutual interdependence between the most influential and active advocates initially. However, both move towards more peripheral advocates subsequently. These results point to the importance of balance between connective and collective action and a movement from the core to periphery to sustain social movements over time. Our findings provide a fresh perspective on inclusive digital organizing that has become central to IS scholarship

Estimating returns to training in the Knowledge Economy : a firm-level analysis of small and medium enterprises

The ongoing digitization of multiple industries has drastically reduced the half-life of skills and capabilities acquired by knowledge workers through formal education. Thus, firms are forced to make significant ongoing investments in training their employees to remain competitive. Existing research has not examined the role of training in improving firm-level productivity of knowledge firms. This paper provides an innovative econometric framework to estimate returns to such employee training investments made by firms. We use a panel dataset of small- to medium-sized Indian IT services firms and assess how training enhances human capital, a critical input for such firms, thereby improving firm revenues. We use econometric approaches based on optimization of the firm’s profit function to eliminate the endogenous choice of inputs common in production function estimations. We find that an increase in training investments is significantly linked to an increase in revenue per employee. Further, marginal returns to training are increasing firm size. Therefore, relatively speaking, large firms benefit more from training. For the median company in our data, we find that a dollar invested in training yields a return of $4.67, and this effect approximately grows 2.5 times for the 75th percentile-sized firm. A variety of robustness checks, including the use of data envelopment analysis, are used to establish the veracity of our results

Fighting the real AI Danger: How to Design Virtuous AI for Virtuous Decision-making

Artificial Intelligence, i.e., complex algorithms that learn to perform functions associated with human minds, such as perceiving, decision-making, and demonstrating creativity. Indeed, more often than not, AI is trained on biased datasets, that is, this data is disproportionately weighted in favor of or against certain individuals or groups of individuals. The roots for such biases are very diverse, sometimes they are technical in nature, but often they originate in the minds of people, making it difficult to identify these biases, e.g. if such a disproportionate weighting is perceived as ‘normal’ by many, while it is still devastating to few. We argue that the use of virtue ethics in AI can help to mitigate the consequences of biases and to help identify such biases. In particular, we aim to assess in multiple online and field experiments how the virtue ‘transparency’ affects an individual’s decision-making and perceptions regarding the AI

Ushering Buyers into Electronic Channels: An Empirical Analysis

Many firms introduce electronic channels in addition to their traditional sales channels and observe increasing buyer adoption rates immediately after the introduction but subsequent declines. Firms must understand the factors that drive channel adoption decisions and how these factors change over time and across buyers. Using panel data pertaining to the purchase histories of 683 buyers over a 43-month period, we estimate a buyer response model that incorporates buyer heterogeneity, channel inertia, and dynamic pricing. We find that channel adoption behavior is both heterogeneous and dynamic, and the firm’s allocation decisions, if not aligned with buyer behavior, can alienate buyers. Based on the parameter estimates from the buyer response model, we propose an alternative channel allocation would enable firms to attract more buyers to the e-channel and improve revenues. Channel adoption increases when firms understand and account for individual buyers’ channel adoption behavior