This study examines the role of project managers' (PM) practical intelligence (PI) in the performance of software offshore outsourcing projects. Based on the extant literature, we conceptualize PI for PMs as their capability to resolve project related work problems, given their long-range and short-range goals; PI is targeted at resolving unexpected and difficult situations, which often cannot be resolved using established processes and frameworks. We then draw on the information processing literature to argue that software offshore outsourcing projects are prone to severe information constraints that lead to unforeseen critical incidents that must be resolved adequately for the projects to succeed. We posit that PMs can use PI to effectively address and resolve such incidents, and therefore the level of PMs' PI positively affects project performance. We further theorize that project complexity and familiarity contribute to its information constraints and the likelihood of critical incidents in a project, thereby moderating the relationship between PMs' PI and project performance.
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.
Despite many success stories, B2B e-commerce penetration remains low. Many firms introduce electronic channels in addition to their traditional sales channels but find that buyer usage of the e-channel over time does not keep up with initial expectations. Firms must understand the underlying factors that drive channel usage 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 dynamic discrete choice model in a B2B setting that (i) recognizes how price, channel inertia, and inventory change over time; (ii) allows buyers to dynamically trade off these factors when making e-channel adoption decisions; and (iii) takes into account buyer heterogeneity. We find that channel usage is both heterogeneous and dynamic across buyers. Our findings reveal the dynamic tradeoff between channel inertia and the adverse price effect, which interact in opposing directions as the e-channel grows more popular over time: price increases resulting from more bids deter buyers, whereas channel inertia built from sampling experience helps retain repeat buyers for the new channel. Second, we find that the buyers' size and diversity influence purchase decisions, and the e-channel appears more attractive to small and/or diversified buyers. Based on our analysis, we postulate that the seller's allocation decisions of products across channels, if not aligned with buyer behavior, can alienate some buyers. Based on the parameter estimates from the buyer response model, we propose an improved channel allocation that enables firms to selectively attract more buyers to the e-channel and improve revenues. Channel acceptance increases as a result of smart allocation when firms understand and account for individual buyers' channel usage behavior.