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The professionalization of IT has long been complicated by disagreement over the appropriate model to employ. Physicians, lawyers, scientists, engineers, artisans, and artists have all one been invoked at one time or another by one group or another as guiding examples for the development of an IT profession. Yet none of these has proved fully convincing. Discusses the different kinds of professional practice which have been likened to IT, considers why it has proved so difficult to settle on a single one, and suggests an alternative way of conceptualizing IT practice.

In selecting the contributions to this special issue, the editors have tried to plot a course that describes the state of the AI field for both the reader unacquainted with AI and for those who are. We have done this by selecting key research papers in the areas of AI that are impacting and will continue to impact libraries and by including sidebars that give context to the research papers. The language of the research papers is not simplified; AI is not a simple field. But neither is it incomprehensible, so if parts of this special issue are found to be difficult, the reader is advised to concentrate on the overall ideas rather than their specific expression.

We conjecture that there are five stages to academic entrepreneurship: motivation, governance, selection, competition, and performance. The process of academic entrepreneurship originates with the motivation of faculty, universities, industry, and government to commercialize knowledge that originates within the university setting. The model conceptualizes that the governance and competitiveness of the commercialized knowledge moderate the mode selection and ultimately the performance of academic entrepreneurship. The conceptual and empirical support for the model are derived from a theory-driven synthesis of articles related to academic entrepreneurship.

This research tests empirically the level of consumer engagement with a product via a nonbrand-controlled platform. The authors explore how social media influencers and traditional celebrities are using products within their own social media Instagram posts and how well their perceived endorsement of that product engages their network of followers.

A total of 226,881 posts on Instagram were analyzed using the Inception V3 convolutional neural network (CNN) pre-trained on the ImageNet dataset to identify product placement within the Instagram images of 75 of the world's most important social media influencers. The data were used to empirically test the relationships between influencers, product placement and network engagement and efficiency.

Influencers achieved higher network engagement efficiencies than celebrities; however, celebrity reach was important for engagement overall. Specialty influencers, known for their “subject” expertise, achieved better network engagement efficiency for related product categories. The highest level of engagement efficiency was achieved by beauty influencers advocating and promoting cosmetic and beauty products.

To maximize engagement and return on investment, manufacturers, retailers and brands must ensure a close fit between the product type and category of influencer promoting a product within their social media posts.

Most research to date has focused on brand-controlled social media accounts. This study focused on traditional celebrities and social media influencers and product placement within their own Instagram posts to extend understanding of the perception of endorsement and subsequent engagement with followers. The authors extend the theory of network effects to reflect the complexity inherent in the context of social media influencers.

The present study develops a multi-theoretic framework of the mechanisms of value creation in interorganizational relationships and of the key factors influencing those mechanisms. The integrative use of several theories in building the model is justified by numerous studies suggesting that a multi-theoretic approach is required to understand the complexity of interorganizational relationships (Gulati, 1998; Osborn & Hagedoorn, 1997; Park et al., 2002). We believe that the relationships between start-up companies and their corporate investors, with each party holding a diversity of strategic and financial objectives, are not less complex than other potential interorganizational relationships. They may therefore also require ideas from several theories to be properly understood. In this study, we build the models applying primarily the resource-based and the knowledge-based views, as well as social capital theory. Ideas from other theoretical approaches are used to complement these theories.

We propose that in addition to its resources and capabilities, a firm’s capital structure and financial health will act as an important determinant of its attractiveness as an alliance partner. Alliances with leveraged firms are prone to unplanned termination due to financial distress, which puts at risk the value embedded in the collaboration. As a result, ceteris paribus, highly leveraged firms will be viewed as less desirable partners in the market for interfirm collaboration when compared to low leverage firms. In support of this proposition, we find that when forming an alliance firms tend to partner with other firms with similar levels of leverage: low-leverage firms partner with other low-leverage firms while high-leverage firms partner with other high-leverage firms, as well as with lower quality ones. Furthermore, we show that alliances with highly leveraged firms are more likely to involve equity participation as a form of ex post protection, especially when they involve partners with relatively lower leverage. Finally, we show that leverage is negatively related to the intensity of alliance activity, suggesting that firms also maintain lower leverage in their capital structure in order to attract potential partners. Overall our results imply that financial policies regarding capital structure have an important role to play in alliancing activity.

During industry emergence, what we call the proto-industry phase, the lack of agreement about legitimate organizational forms between audiences and firms is a key problem. We develop an ecological model of emerging institutional pressures among audiences and firms during the emergence of new industries to understand these challenges. We develop a theoretical framework that includes mimetic, normative, and coercive pressures, deriving propositions linking them with survival and growth. We use simulation methodology to test these propositions, finding strong support for these predictions. We close by exploring some conclusions and implications of our model for both theory and practice.

We develop and test a model of how a software firm's business strategy (product scope and market scope) interacts with the firm's network position (alliance degree and structural holes) to impact performance. We test the joint-effects hypotheses on a sample 359 packaged software firms that have entered into 5,489 alliances involving 2,849 distinct firms during the time period, 1990–2002. While prior studies have demonstrated the importance of network positions as a determinant of firm strategy and performance, this chapter begins to examine the performance effects of how a firm's business strategy and network positions interact. We find support for three of the four hypotheses lending empirical support for our theoretical model. We develop implications for network-based perspectives of strategy and outline areas for further research.