Me or information technology? Adoption of artificial intelligence in the delegation of personal strategic decisions

Artificial intelligence is revolutionizing the way we do business. Whereas both the adoption of technological innovations and strategic decision making have been subject to prior research, this experimental study is first to combine both. To understand the impact of choice complexity and cognitive perceptions on the willingness to delegate a strategic decision to an algorithm, we conducted an experiment with 310 participants. We find that although choice complexity has no effect, participants with low levels of situational awareness are more likely to delegate. The findings augment research on choice complexity in decision making by emphasizing the relevance of cognitive perceptions.     

On Some Characteristics of Gaussian Covariance Functions

The concepts of isotropy/anisotropy and separability/non‐separability of a covariance function are strictly related. If a covariance function is separable, it cannot be isotropic or geometrically anisotropic, except for the Gaussian covariance function, which is the only model both separable and isotropic. In this paper, some interesting results concerning the Gaussian covariance model and its properties related to isotropy and separability are given, and moreover, some examples are provided. Finally, a discussion on asymmetric models, with Gaussian marginals, is furnished and the strictly positive definiteness condition is discussed.     

How to approach business model innovation: the role of opportunities in times of (no) exogenous change

Firms need to show dynamic adaptability and innovate their business models to achieve survival and growth, in particular when they are exposed to high levels of exogenous change. However, business model innovation also takes place in the absence of exogenous change. We know relatively little about firms’ approaches to opportunities for business model innovation in both of these environmental settings. The objective of this study is to understand how firms exposed to various environmental conditions explore and exploit business model innovation opportunities. In a qualitative multiple case analysis, the study compares the approaches to business model innovation used by four firms exposed to high levels of exogenous change with those of four firms operating in the absence of exogenous change. The findings are contrasted with entrepreneurial opportunity discovery and creation theories. The results reveal that firms exposed to high levels of exogenous change focus on discovering objective opportunities, whereas firms operating in the absence of exogenous change concentrate on creating opportunities for business model innovation. However, this study revealed that when the firms further explore and exploit these opportunities, they tend to combine the behaviours that the two opportunity perspectives suggest. This study contributes to the literature on business model innovation by emphasising the relationship between environmental context and approach to business model innovation and by reinforcing the link with entrepreneurship theory.     

Firm heterogeneity, productivity and spatially bounded knowledge externalities

In this paper, we argue that conceptually disentangling the ‘context versus composition’ aspects of regional growth is a multilevel issue. By applying multilevel models (also called random-effects models), we show (1) the importance of considering firm-specific characteristics simultaneously with region-specific characteristics, as we find that a large part of what is traditionally assigned to the impact of the region should be assigned to firm-specific characteristics and (2) that existing single-level methodologies can be problematic, as they are vulnerable to the charge of estimating significance levels that are too liberally assigned and promote exaggerations. This is illustrated empirically by showing that single-level approaches would lead to the conclusion that innovation spillovers are highly significant in a setting of Dutch urban growth differentials, while multilevel analyses shows less liberally assigned significance levels. We conclude that multilevel-effect models better fit research questions that combine firm and spatial characteristics simultaneously, especially because they allow firm-specific characteristics to be differently linked to their regional contexts.