This paper addresses two fundamental problems in the absorptive capacity (AC) literature: conceptual ambiguity on what AC is and a lack of synthesized empirical findings showing how AC matters for firm outcomes. We take a two-pronged approach to address these problems: (1) conceptual distillation of the literature to discern the core AC dimensions, outcomes, and contingent external knowledge conditions and (2) meta-analysis of the empirical literature to synthesize the findings. For conceptual distillation, we identify three dimensions of AC: absorptive effort (i.e., the knowledge-building investments made by a firm), absorptive knowledge base (i.e., the current knowledge stock of a firm), and absorptive process (i.e., a firm's internal procedures and practices related to knowledge diffusion). We develop these dimensions by explicating their theoretical roots, functions, mechanisms, and corresponding measures. Leveraging the conceptual distillation, we conduct meta-analyses of the empirical literature and synthesize key
This paper proposes a two-stage scoring approach to help lenders decide their fund allocations in the peer-to-peer (P2P) lending market. The existing scoring approaches focus on only either probability of default (PD) prediction, known as credit scoring, or profitability prediction, known as profit scoring, to identify the best loans for investment. Credit scoring fails to deliver the main need of lenders on how much profit they may obtain through their investment. On the other hand, profit scoring can satisfy that need by predicting the investment profitability. However, profit scoring completely ignores the class imbalance problem where most of the past loans are non-default. Consequently, ignorance of the class imbalance problem significantly affects the accuracy of profitability prediction. Our proposed two-stage scoring approach is an integration of credit scoring and profit scoring to address the above challenges. More specifically, stage 1 is designed as credit scoring to identify non-default loans while the imbalanced nature of loan status is considered in PD prediction. The loans identified as non-default are then moved to stage 2 for prediction of profitability, measured by internal rate of return. Wide and deep learning is used to build the predictive models in both stages to achieve both memorization and generalization. Extensive numerical studies are conducted based on real-world data to verify the effectiveness of the proposed approach. The numerical studies indicate our two-stage scoring approach outperforms the existing credit scoring and profit scoring approaches.
High-achieving employees, the "stars" of an organization, are widely credited with producing indispensable, irreplaceable, value-enhancing contributions. From the recruitment of celebrity CEOs to the fierce competition for star scientists, and from lucrative contracts for sports icons to out-sized bonuses for top salespeople, human capital strategies have long promoted the importance of star performers. Sixty years of research on stars has witnessed a wide array of contexts, levels of analysis, and sub-dimensions, much of which is focused on the accomplishments of these alphatail individuals. More recently, however, scholars have begun to draw varied conclusions regarding both the favorable and unfavorable impacts of star performers, leading to a balkanization of the perspectives comprising the stream. Our review of the multi-disciplinary work on stars synthesizes disparate studies, settles definitional problems, and integrates complementary factors into a coherent formative construct. Through this, we foster the development of a research agenda concerning the manner in which star performers are, by their very nature, simultaneously red giants and black holes, the precise balance of which is fertile soil for future inquiry.
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