Excessive Volatility is Also a Feature of Individual Level Forecasts

The excessive volatility of prices in financial markets is one of the most pressing puzzles in social science. It has led many to question economic theory, which attributes beneficial effects to markets in the allocation of risks and the aggregation of information. In exploring its causes, we investigated to what extent excessive volatility can be observed at the individual level. Economists claim that securities prices are forecasts of future outcomes. Here, we report on a simple experiment in which participants were rewarded to make the most accurate possible forecast of a canonical financial time series. We discovered excessive volatility in individual-level forecasts, paralleling the finding at the market level. Assuming that participants updated their beliefs based on reinforcement learning, we show that excess volatility emerged because of a combination of three factors. First, we found that submitted forecasts were noisy perturbations of participants’ revealed beliefs. Second, beliefs were updated using a prediction error based on submitted forecast rather than revealed past beliefs. Third, in updating beliefs, participants maladaptively decreased learning speed with prediction risk. Our results reveal formerly undocumented features in individual-level forecasting that may be critical to understand the inherent instability of financial markets and inform regulatory policy.     

The new WTP: Willingness to participate

A key concept underlying competitive strategy is that of WTP, representing the consumer's ‘willingness to pay’ a premium price for goods or services. Through branding and other efforts, companies strive to push their message out and create a high willingness to pay, whereby consumers feel there are few or no substitutes for what these companies are selling. Social media, however, are making push-based marketing anachronistic. Users of social media typically eschew professional communications forced on them by faceless and impersonal organizations, in favor of more personal conversations. These individuals seek greater engagement with their preferred brands, and involvement—with or without the company's approval—in creating brand personalities. Their affinity for these preferred brands might well auger the dawn of a new WTP: willingness to participate. This article presents a model of consumer engagement through social media, and argues for re-conceptualizing WTP by utilizing a series of examples which show how companies that engage consumers via social media (e.g., Facebook, Twitter, YouTube) stand to reap the benefits of long-term competitive advantages.     

Supra-national regulation for supra-national telecommunications carriers?

The article reviews the extraordinary number of international ventures by major telecommunications organizations and explores problems for traditional national regulation that these global networks create. A theoretical model is defined that illustrates the adjustment process of different national policies to each other. The choice of various supra-national regulatory arrangements is shown to be affecting substantive policy. The article then discusses options for coordination and concludes that the concrete problems created by supra-national carriers do not justify the hidden cost of elaborate mechanisms of supra-national policy. Since such coordination has traditionally been used to prop up monopoly arrangements, a better approach for the foreseeable future is to encourage more national policy experimentation and focus less on international policy coordination.     

Examining the context-specific reasons and adoption of artificial intelligence-based voice assistants: A behavioural reasoning theory approach

AI-based voice assistants are some of the most popular consumer products of artificial intelligence technology. This study investigates the influence of the reasoning process on overall consumer attitudes and adoption intentions towards AI-based voice assistants. Behavioural reasoning theory was applied to hypothesize relationships between values, context-specific reasons, attitudes, and adoption intention. Partial least squares structural equation modelling was used to test the research model and hypotheses using data from 550 potential consumers in India. This research suggests a positive influence of consumers' ‘reasons for’ on their attitude and adoption intentions towards AI-based voice assistants, as well as a favourable impact of consumers' values on ‘reasons for’. This study discovered that ‘reasons for’ and ‘reasons against’ are not only opposites but are entirely autonomous and have an independent impact on consumers' attitudes towards AI-based voice assistants. This research improved our understanding of AI-based product adoption by identifying the paramount role of consumers' values and the ‘reasons for’ and ‘reasons against’ in shaping their attitudes and adoption intentions towards AI-based voice assistants.