This paper identifies strategic and economic determinants affecting a defendant's decision of settlement during litigation by using 3018 cases in the Delaware District Court in the United States. The results show that the potential for settlement is high if the litigation cost to be borne by defendant increases. While, defendant's lawsuit experience and a Markman hearing process clarifying the scope of the patent claims through both parties' evidences induce the defendant's trial decision. This paper also identified that the defendant may make strategically different decisions, depending on the technological field and interactions between the determinants.
Abstract Amidst the overall trend of convergence in information technology, device convergence is noteworthy. This study looks at the possible direction of device convergence based on consumer preferences for the main attributes of the mobile terminal of the future. Conjoint analysis and a mixed logit model using a Bayesian approach with Gibbs sampling are used to learn consumer preferences. Results show that consumers generally prefer a keyboard and a medium-sized display, although at present most consumers are indifferent to whether the terminal provides high-quality Internet service and to whether it operates many kinds of application programs or programs originally designed for personal computers. Given the heterogeneity of consumer preferences, partial, rather than perfect, device convergence is anticipated. Implications for the future of device convergence and how it will affect other types of convergence are drawn. Notes Blackman (Citation1998) dealt with the convergence phenomenon as a whole, treating convergence between telecommunications and media, and showing some implications for new, appropriate regulatory frameworks. Messerschmitt (Citation1996) dealt with convergence between computing and telecommunications technologies, specifically in terms of networked computing applications, and emphasized the importance of user-to-user applications in convergence. Mueller (Citation1999) also dealt with convergence as a whole. In his working paper, in regard to device convergence, he insisted that because of the new media ecology and technological improvements, devices and applications would become more diverse and specialized while becoming more interoperable. Yoffie (Citation1997) referred to past examples of device convergence such as between the television and the videocassette recorder and emphasized knowing whether consumer needs exist or not to predict success of device convergence. For details, see Train (Citation2003, pp. 302–6).
This study assessed the effect of simultaneous implementations of different intellectual property (IP) protection mechanisms on a firm's product innovation performance (PIP). The study categorized seven widely‐used IP protection mechanisms (IPPMs) into two groups: formal and informal. Complementarity was then tested within and between the formal and informal groups of IPPMs. The result showed that there existed complementarity when multiple IPPMs were implemented from the same groups. Throughout an additional analysis on the moderating effect of the industrial complexity in technology, it was found that the ‘between groups' combination effect was also existed but varied from even negative to positive concluding that industrial complexity of technology moderates the effects of combinations of IPPMs on a firm's PIP. These results imply that the use of multiple IPPMs is effective but the effect varies by the technological complexity of industry.
This paper extends the research on the R&D—patent relationship by distinguishing the factors affecting R&D productivity (which is defined as ‘efficiency parameters) from the factors associated with the propensity of firms to patent, and estimates the impacts of the factors on innovative output. A data set from 1255 firms with nonzero R&D expenditures in Korea was studied, and the results show that efficiency parameters such as in-depth patent searches and revenue splitting policy for employee-inventors influence firm’s innovative output through ‘R&D productivity’ effect. These results mean that we can improve firm’s innovation output by changing efficiency parameters through a firm’s investment or government support in a relatively short period.
'%3e%3cg id='b'%3e%3cpath id='a' stroke='%23000' stroke-width='2' d='M-6-25H6m-12 3H6m-12 3H6'/%3e%3cuse xlink:href='%23a' y='44'/%3e%3c/g%3e%3cpath stroke='%23fff' d='M0 17v10'/%3e%3ccircle r='12' fill='%23cd2e3a'/%3e%3cpath fill='%230047a0' d='M0-12A6 6 0 0 0 0 0a6 6 0 0 1 0 12 12 12 0 0 1 0-24z'/%3e%3c/g%3e%3cg transform='rotate(-123.69)'%3e%3cuse xlink:href='%23b'/%3e%3cpath stroke='%23fff' d='M0-23.5v3M0 17v3.5m0 3v3'/%3e%3c/g%3e%3c/svg%3e)