Activity Overinvestment: The Case of R&D

The literature on corporate diversification has often argued for and established the case that companies often overdiversify in a product market sense – i.e. enter into unrelated product markets where they may not fully cover their cost of capital. Yet, even without engaging in unrelated diversification, managers need to make resource allocation decisions to a variety of activities that a company conducts to consummate its business. In this article we focus on Research and Development (R&D) activity and we discuss the effects that the uncertainty, boundary ambiguity, feedback latency, R&D lumpiness and legitimacy that characterize technological contexts can have in making overinvestment in R&D likely. Specifically, in this article we a) draw attention to the construct of activity overinvestment, and specifically R&D overinvestment, b) use the received literature to argue that there exists a prima facie case for examining this construct and its antecedents in order to evaluate the extent and implications of R&D overinvestment, and c) make the more general case that the resource allocation literature needs to study the issue of activity overinvestment systematically

Significado do trabalho: um estudo entre trabalhadores inseridos em organizações formais

Semelhanças e diferenças entre o estudo do Meaning of Working International Research Team (1987) para oito países e de Soares (1993) para o Brasil, com base numa aplicação dos dados relativos ao contexto brasileiro

Angel investor's selection criteria: a comparative institutional perspective

Despite the important role of angel investors as critical financial providers for new ventures, little is known regarding how institutions make their investment decisions. While angels make decisions based on selection criteria during the first stage, they are also embedded within and affected by different institutional settings and as a result weight these criteria differently than other investors. We compare angel investors' selection criteria in China and Denmark using the comparative institutional perspective. We use a policy capturing approach and hierarchy linear modeling, revealing that since Chinese angels are embedded within relationship-based institutional settings they tend to reply more on strong ties such as family and friends in management team, as well as weighting risks less compared to Danish angels operating within more rule-based institutional contexts

More-or-less elicitation (MOLE): reducing bias in range estimation and forecasting

Biases like overconfidence and anchoring affect values elicited from people in predictable ways – due to people’s inherent cognitive processes. The More-Or-Less Elicitation (MOLE) process takes insights from how biases affect people’s decisions to design an elicitation process to mitigate or eliminate bias. MOLE relies on four, key insights: 1) uncertainty regarding the location of estimates means people can be unwilling to exclude values they would not specifically include; 2) repeated estimates can be averaged to produce a better, final estimate; 3) people are better at relative than absolute judgements; and, 4) consideration of multiple values prevents anchoring on a particular number. MOLE achieves these by having people repeatedly choose between options presented to them by the computerised tool rather than making estimates directly, and constructing a range logically consistent with (i.e., not ruled out by) the person’s choices in the background. Herein, MOLE is compared, across four experiments, with eight elicitation processes – all requiring direct estimation of values – and is shown to greatly reduce overconfidence in estimated ranges and to generate best guesses that are more accurate than directly estimated equivalents. This is demonstrated across three domains – in perceptual and epistemic uncertainty and in a forecasting task.Matthew B. Welsh, Steve H. Beg

Anchoring: A Valid Explanation for Biased Forecasts When Rational Predictions are Easily Accessible and Well Incentivized?

Behavioral biases in forecasting, particularly the lack of adjustment from current values and the overall clustering of forecasts, are increasingly explained as resulting from the anchoring heuristic. Nonetheless, the classical anchoring experiments presented in support of this interpretation lack external validity for economic domains, particularly monetary incentives, feedback for learning effects and a rational strategy of unbiased predictions. We introduce an experimental design that implements central aspects of forecasting to close the gap between empirical studies on forecasting quality and the laboratory evidence for anchoring effects. Comprising more than 5,000 individual forecasts by 455 participants, our study shows significant anchoring effects. Without monetary incentives, the share of rational predictions drops from 42% to 15% in the anchor's presence. Monetary incentives reduce the average bias to one-third of its original value. Additionally, the average anchor bias is doubled when task complexity is increased, and is quadrupled when the underlying risk is increased. The variance of forecasts is significantly reduced by the anchor once risk or cognitive load is increased. Subjects with higher cognitive abilities are on average less biased toward the anchor when task complexity is high. The anchoring bias in our repeated game is not influenced by learning effects, although feedback is provided. Our results support the assumption that biased forecasts and their specific variance can be ascribed to anchoring effects