How do markets evolve? Why are some innovations picked up straightaway whilst others take years to be commercialized? Are there first-mover advantages? Why do we behave with 'irrational exuberance' ...in the early evolution of markets as was the case with the dot.com boom? Paul Geroski is a leading economist who has taught economics to business school students, managers, and executives at the London Business School. In this book he explains in a refreshingly clear style how markets develop. In particular he stresses how the early evolution of markets can significantly shape their later development and structure. His purpose is to show how a good grasp of economics can improve managers' business and investment decisions. Whilst using the development of the Internet as a case in point, Geroski also refers to other sectors and products, for example cars, television, mobile phones, and personal computers. This short book is an ideal introduction for managers, MBA students, and the general reader wanting to understand how markets evolve. Available in OSO: http://www.oxfordscholarship.com/oso/public/content/economicsfinance/0199248893/toc.html
There are numerous biological and small‐molecule therapies currently under investigation for the prevention and treatment of COVID‐19. This article focuses on antibody therapies that target the virus ...itself, including convalescent plasma and monoclonal antibodies against SARS‐CoV‐2, and discusses their efficacy in clinical trials and likely future role.
Task-Dependent Algorithm Aversion Castelo, Noah; Bos, Maarten W.; Lehmann, Donald R.
Journal of marketing research,
10/2019, Letnik:
56, Številka:
5
Journal Article
Recenzirano
Research suggests that consumers are averse to relying on algorithms to perform tasks that are typically done by humans, despite the fact that algorithms often perform better. The authors explore ...when and why this is true in a wide variety of domains. They find that algorithms are trusted and relied on less for tasks that seem subjective (vs. objective) in nature. However, they show that perceived task objectivity is malleable and that increasing a task's perceived objectivity increases trust in and use of algorithms for that task. Consumers mistakenly believe that algorithms lack the abilities required to perform subjective tasks. Increasing algorithms' perceived affective human-likeness is therefore effective at increasing the use of algorithms for subjective tasks. These findings are supported by the results of four online lab studies with over 1,400 participants and two online field studies with over 56,000 participants. The results provide insights into when and why consumers are likely to use algorithms and how marketers can increase their use when they outperform humans.
Few observers are unimpressed by the economic ambition of China or by the nation's remarkable rate of growth. But what does the future hold? This meticulously researched book closely examines the ...strengths and weaknesses of the Chinese economic system to discover where the nation may be headed and what the Chinese experience reveals about emerging market economies. The authors find that contrary to popular belief, cutting edge innovation is not a prerequisite for sustained economic vitality-and that China is a perfect case in point.
Although there are opposing theoretical arguments on the relationship between the strength of a country's employment protection laws (EPLs) and innovation, empirical evidence tilts towards a positive ...relationship. However, research has mainly focused on the early stages of the innovation process, such as R&D and patenting. This study examines the role of EPLs in the later stages of the innovation process: the commercialization of new products. In particular, we focus on EPLs' relationship with two different new product commercialization outcomes: the launch and subsequent sales of new products. Using data on small European firms, we find that, controlling for invention, stricter EPLs are negatively associated with firms' likelihood of launching new products, but positively associated with the sales from new products. We discuss the implications of our results for theory and practice.
•Past research mainly focuses on employment protection laws (EPLs) and invention.•Focus on EPLs and two aspects of new product commercialization: launch and sales.•EPLs relate negatively to new product launch decisions.•However, EPLs relate positively to new product sales.•The results hold when controlling for, and are independent of, R&D intensity.
A chemistry professor explains the mechanism of the copper(I)‐catalyzed alkyne–azide cycloaddition (CuAAC) while suggesting that by interrupting the click reaction, it is possible to exploit the ...reactivity of the copper metallacycle intermediate to synthesize new products. The students who are listening are imagining novel synthetic pathways and finding ways to access products that are typically hard to obtain. More information can be found in the Review by M. Giustiniano, G. C. Tron and co‐workers (DOI: 10.1002/chem.202303844).
Research on growth of innovations introduced to the market has gradually shifted its focus from aggregate-level diffusion to exploring how growth is influenced by a given social network structure's ...characteristics. In this paper, we critically review this branch of literature. We argue that the growth of an innovation in a social network is shaped by the network's structure. Borrowing from the field of industrial organization in economics, which defines itself as the study of the effect of market structure on market performance, we describe this new wave of research on growth of innovations as the effect of social network structure on innovation performance. Hence, social network structural characteristics should be incorporated into research on new product growth as well as into managerial marketing decisions such as targeting and new product seeding.
We review how social network structure influences innovations' market performance. Specifically, we discuss (1) a networks' global characteristics, namely average degree, degree distribution, clustering, and degree assortativity; (2) dyadic characteristics, or the relationships between pairs of network members, namely tie strength and embeddedness; (3) intrinsic individual characteristics, namely opinion leadership and susceptibility; and (4) location-based individual characteristics, namely the degree centrality, closeness centrality, and betweenness centrality of an individual network member.
Overall, we find that growth is particularly effective in networks that demonstrate the “3 Cs”: cohesion (strong mutual influence among its members), connectedness (high number of ties), and conciseness (low redundancy). We identify gaps in current knowledge, discuss the implications on managerial decision making, and suggest topics for future research.
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