Trans-specialization understanding in international technology alliances: The influence of cultural distance

In the information age, the firm's performance hinges on combining partners' specialist knowledge to achieve value co-creation. Combining knowledge from different specialties could be a costly process in the international technology alliances (ITAs) context. We argue that the combination of different specializations requires the development of "trans-specialization understanding" (TSU) instead of the internalization of partners' specialist knowledge. This article examines the extent to which inter-firm governance in ITAs shapes TSU, and whether the development of TSU is endangered by cultural distance. We hypothesize that relational governance, product modularity, and cultural distance influence TSU development, which in turn influences firm performance. We collected data from 110 non-equity ITAs between software and hardware firms participating in the mobile device sector. We analyzed the data using partial least squares path modeling. Our findings suggest that TSU largely depends on product modularity and relational governance in alliances. However, while cultural distance negatively moderates the path from relational governance to TSU, it has no effect on the relationship between product modularity and TSU. Based on this, we conclude that product modularity can substitute for relational governance when strong relational norms are not well-developed in international alliances. Thus cultural distance does not invariably amount to a liability in ITAs

Analyzing quadratic effects of formative constructs by means of variance-based structural equation modelling

Together with the development of information systems research, there has also been increased interest in non-linear relationships between focal constructs. This article presents six Partial Least Squares-based approaches for estimating formative constructs' quadratic effects. In addition, these approaches' performance is tested by means of a complex Monte Carlo experiment. The experiment reveals significant and substantial differences between the approaches. In general, the performance of the hybrid approach as suggested by Wold (1982) is most convincing in terms of point estimate accuracy, statistical power, and prediction accuracy. The two-stage approach suggested by Chin et al (1996) showed almost the same performance; differences between it and the hybrid approach - although statistically significant - were unsubstantial. Based on these results, the article provides guidelines for the analysis of nonlinear effects by means of variance-based structural equation modelling