While strategy scholars primarily focus on internal firm capabilities and network scholars typically examine network structure, we posit that firms with superior network structures may be better able to exploit their internal capabilities and thus enhance their performance. We examine how innovative capabilities-both those of focal firms and those they access through their networks-influence the performance of Canadian mutual fund companies. We find that a firm's innovative capabilities and its network structure both enhance firm performance, while the innovativeness of its contacts does not do so directly. Innovative firms that also bridge structural holes get a further performance boost, suggesting that firms need to develop network-enabled capabilities-capabilities accruing to innovative firms that bridge structural holes.

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Knowledge—which is closely linked to firm innovativeness—is accessed across organizational boundaries and geographic space via networks operating at different levels of analysis. However, we know tantalizingly little about the comparative influence of geography on knowledge flow across organizational boundaries over different types of ties, despite warnings that research needs to account for the geographic context of ties to fully understand causal relationships. Using a combination of primary and secondary data on 77 Canadian mutual fund companies, we find that institutional-level ties are valuable in knowledge transmission only when such ties are geographically proximate. Organization-level ties fail to act as transmitters of knowledge, regardless of geographic location. Interestingly, we find that geographically distant individual-level friendship ties are superior conduits for knowledge flow, which suggests they span “geographic holes.”

Four types of bankruptcy prediction models based on financial statement ratios, cash flows, stock returns, and return standard deviations are compared. Based on a sample of bankruptcies from 1980 to 1991, results indicate that no existing model of bankruptcy adequately captures the data. During the last fiscal year preceding bankruptcy, none of the individual models may be excluded without a loss in explanatory power. If considered in isolation, the cash flow model discriminates most consistently two to three years before bankruptcy. By comparison, the ratio model is the best single model during the year immediately preceding bankruptcy. Quasi-jack-knifing procedures suggest that none of the models can reliably predict bankruptcy more than two years in advance.Other research which compares alternative prediction models on the same data includes Collins (1980) and Hamer (1983).

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