In this conceptual paper, we use the multiple agency theory to explain the governance issues in cross-sector partnerships. In doing so, we identify five typologies of cross-sector partnerships, each distinct from one another in terms of the governance structure. We also discuss three problems that occur in these relationships, mainly differences in institutional logic, temporal differences, and free-riding and opportunistic behavior, as well as how these problems affect each type of cross-sector partnership. These findings make several significant contributions to the literature and theories. First, this is one of the few studies to use agency theory to examine the governance structures and problems in cross-sector partnerships. Second, our study further contributes to solidifying our understanding of the agency problems arising in cross-sector collaborations. Lastly, our study confirms their presence in other types of cross-sector partnerships as well and we maintain that these problems do not affect each type of cross-sector partnership in the same way, leading us to offer more nuanced implications for researchers and practitioners.

Purpose: Researchers have shown that aesthetic judgments of artworks depend on contexts, such as the authenticity of an artwork (Newman & Bloom, 2011) and an artwork’s location of display (Kirk et al., 2009; Silveira et al., 2015). The present study aims to examine whether contextual information related to the creator, such as whether an artwork was created by a human or artificial intelligence (AI), influences viewers’ preference judgments of an artwork. Methods: Images of Impressionist landscape paintings were selected as human-made artworks. AI-made artwork stimuli were created using Google’s Deep Dream Generator by mimicking the Impressionist style via deep learning algorithms. Participants performed a preference rating task on each of the 108 artwork stimuli accompanied by one of the two creator labels. After this task, an art experience questionnaire (AEQ) was given to participants to examine whether individual differences in art experience influence their preference judgments. Results: Setting AEQ scores as a covariate in a two-way ANCOVA analysis, the stimuli with the human-made context were preferred over the stimuli with the AI-made context. Regarding the types of stimuli, the viewers preferred AI-made stimuli to human-made stimuli. There was no interaction effect between the two factors. Conclusion: These results suggest that preferences for visual artworks are influenced by the contextual information of the creator when the individual differences in art experience are controlled.