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This study examines why firms fail or survive in the volatile software industry. We provide a novel perspective by considering how software firms' capabilities and their competitive actions affect their ultimate survival. Drawing on the resource based view (RBV) we conceptualize capabilities as a firm's ability to efficiently transform input resources into outputs, relative to its peers. We define three critical capabilities of software producing firms: Research and Development (RD), Marketing (MK) and Operations (OP), and hypothesize that in the dynamic, high technology software industry, RD and MK capabilities are most important for firm survival. We then draw on the competitive dynamics literature to theorize that competitive actions distinguished by a greater emphasis on innovation-related moves will increase firm survival more than those emphasizing resource-related moves. Finally, we postulate that firms' capabilities will complement their competitive actions in affecting firm survival. Our empirical evaluation examines a cross-sectional, time series panel of 5,827 observations on 870 software companies from 1995 to 2007. We use a stochastic frontier production function to measure the capability for each software firm in each time period. We then use the Cox proportional hazard regression technique to relate capabilities and competitive actions to software firms' failure rates. Unexpectedly, our results reveal that higher OP capability increases software firm survival more than higher MK and RD capabilities. Further, firms with a greater emphasis on innovation-related than resource-related competitive actions have a greater likelihood of survival and this likelihood increases even further when these firms have higher MK and OP capabilities. Additional analyses of sub-sectors within the software industry reveal that firms producing visual applications (e.g., graphical and video game software) have the highest MK capability but the lowest OP and RD capabilities and make twice as many innovation-related as resource-related moves. These firms have the highest market values but the worst Altman Z scores, suggesting that the firms are valued highly but also are at high risk for failure, and indeed the firms in this sector fail at a greater rate than expected. In contrast, firms producing traditional decision-support applications and infrastructure software have different capabilities and make different competitive moves. Our findings suggest that the firms that persist and survive over the long term in the dynamic software industry are able to capitalize on their competitive actions due to their greater capabilities, and particularly, OP capabilities.

This paper studies a buyback contract in the Stackelberg framework of a manufacturer (leader) selling to a price-setting newsvendor retailer (follower). Using an analytical model that focuses on a multiplicative demand form, we generalize previous results and produce new structural insights. A novel transformation technique first enables us to establish the unimodality of the profit functions for both channel partners, under relatively mild assumptions. Further analysis identifies the necessary and sufficient condition under which the optimal contract for the manufacturer (wholesale and buyback prices) is distribution free, i.e., independent of the uncertainty in customer demand. A specific instance of the above condition is also necessary and sufficient for a no-buyback contract to be optimal from the manufacturer's perspective. We then prove that the optimal performance of the decentralized channel for distribution-free buyback contracts depends only on the curvature of the deterministic demand part. In addition, some of the optimal decisions and relevant profit ratios for buyback contracts in our setting are shown to be identical to those for their deterministic price-only counterparts.price-setting newsvendor, buyback contract, supply chain performance, demand curvature

Since December 2019, the novel COVID-19 outbreak has spread rapidly around the globe and infected millions of people. Although the major transmission route of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is considered to be airborne droplets and close contact, the ocular transmission route has been reported with great concern. The current work summarises the characteristics of SARS-CoV-2, the ocular distribution of the major SARS-CoV-2 binding protein, and the experimental and clinical evidence of the ocular transmission route. Although it seems that the likelihood of the ocular surface being an infection gateway is low, SARS-CoV-2 infection or transmission via the ocular surface may cause conjunctivitis and other ocular discomfort. Therefore, good eye protection is an essential safeguard procedure, especially for medical staff.

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This paper examines a multiproduct dynamic investment model for making technology choices and expansion decisions over a finite planning horizon. The motivation for our problem comes from recent developments in the field of flexible technology such as CAD, CAM, and CIM that permit firms to invest in these more expensive, flexible technologies to provide a competitive edge in the form of an ability to respond rapidly to changing product mix. On the other hand, more specialized (dedicated) equipment may be less costly. The decisions on appropriate mixes of dedicated and flexible capacity involve many complex considerations such as economies of scale, demand patterns, and mix flexibility. We formulate the problem as a mathematical program with the objective of minimizing total investment cost. Since the problem is difficult to solve optimally, we develop a two-phased approach and present heuristics to obtain good expansion schedules. These procedures are based on an easily solvable sequence of subproblems derived from the planning problem. Our computational results suggest that these methods work well and provide acceptable solutions with reasonable effort.

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