Distributed Leadership: A Review of Literature

A review of empirical research studies of distributed leadership from public and private sector settings completed since 1995

Carry on winning: No selection effect

AbstractThe methods proposed by Demaree, Weaver and Juergensen (2014) are not the most appropriate for testing for the presence of a selection effect. We use a simple and straightforward method to demonstrate that the data are not consistent with such an effect

Outcome feedback reduces over-forecasting of inflation and overconfidence in forecasts

Survey respondents over-forecast inflation: they expect it to be higher than it turns out to be. Furthermore, people are generally overconfident in their forecasts. In two experiments, we show that providing outcome feedback that informs people of the actual level of the inflation that they have forecast reduces both over-forecasting and overconfidence in forecasts. These improvements were preserved even after feedback had been withdrawn, a finding that indicates that they were not produced because feedback had a temporary incentive effect but because it had a more permanent learning effect. However, providing forecasters with more outcome feedback did not have a greater effect. Feedback appears to provide people with information about biases in their judgments and, once they have received that information, no additional advantage is obtained by giving it to themagain. Reducing over-forecasting also had no clear effect on overall error. This was because providing outcome feedback after every judgment also affected the noise or random error in forecasts, increasing it by a sufficient amount to cancel out the benefits provided by the reduction in over-forecasting

Point, interval, and density forecasts: Differences in bias, judgment noise, and overall accuracy

There are three main ways in which judgmental predictions are expressed: point forecasts; interval forecasts; probability density forecasts. Do these approaches differ solely in terms of their simplicity of elicitation and the detail they provide? We examined error in values of the central tendency extracted from these three types of forecast in a domain in which all of them are used: lay forecasts of inflation. A first experiment using a between-participant design showed that the mean level of forecasts and the bias in them are unaffected by the type of forecast but that judgment noise (and, hence, overall error) is higher in point forecasts than in interval or density forecasts. A second experiment replicated the difference between point and interval forecasts in a within-participant design (of the sort used in inflation surveys) and showed no effect of the order in which different types of forecast are made but revealed that people are more overconfident in interval than in point forecasts. A third experiment showed that volatility in past data increases bias in point but not interval forecasts, and that taking the average of two point forecasts made by an individual reduces judgment noise to the level found in interval forecasting

Forecasting from time series subject to sporadic perturbations : effectiveness of different types of forecasting support

How effective are different approaches for the provision of forecasting support? Forecasts may be either unaided or made with the help of statistical forecasts. In practice, the latter are often crude forecasts that do not take sporadic perturbations into account. Most research considers forecasts based on series that have been cleansed of perturbation effects. This paper considers an experiment in which people made forecasts from time series that were disturbed by promotions. In all conditions, under-forecasting occurred during promotional periods and over-forecasting during normal ones. The relative sizes of these effects depended on the proportions of periods in the data series that contained promotions. The statistical forecasts improved the forecasting accuracy, not because they reduced these biases, but because they decreased the random error (scatter). The performance improvement did not depend on whether the forecasts were based on cleansed series. Thus, the effort invested in producing cleansed time series from which to forecast may not be warranted: companies may benefit from giving their forecasters even crude statistical forecasts. In a second experiment, forecasters received optimal statistical forecasts that took the effects of promotions into account fully. This increased the accuracy because the biases were almost eliminated and the random error was reduced by 20%. Thus, the additional effort required to produce forecasts that take promotional effects into account is worthwhile

Effects of shopping addiction on consumer decision-making: Web-based studies in real time

Background and aims: Most research into compulsive buying has focused on its causes: questionnaires have been used to study its association with various factors assumed to be important in its etiology. Few studies have dealt with the effects of being a compulsive buyer on shopping decisions. Also, processes underlying compulsive buying are dynamic but questionnaires give access only to a retrospective view of them from the standpoint of the participant. The aim of the current study was to investigate the decision processes underlying compulsive buying. Methods: Two simulated shopping experiments, each with over 100 participants, were used to compare the decision processes of compulsive shoppers with those of non-compulsive shoppers. This approach allowed us to measure many features of consumer decision-making that are relevant to compulsive shopping. Results: Compulsive shoppers differed from general shoppers in six ways: choice characteristics, searching behavior, overspending, budget-consciousness, effects of credit card availability, and emotional responses to overspending. Conclusions: Results are consistent with the view that compulsive buying, like other behavioral addictions, develops because the cognitive system under-predicts the extent of post-addiction craving produced by emotional and visceral processes

Compulsive Buying: Obsessive Acquisition, Collecting or Hoarding?

We report two investigations into compulsive shopping behavior. The first showed that compulsive shoppers are obsessive in acquiring certain classes of product. Compared to normal shoppers, they spend more money on these products and shop for them more frequently. The second study showed that an obsession with acquiring these products rather than a desire to collect or hoard them is a significant factor in producing compulsive shopping behavior. This is consistent with the notion that compulsive shopping is an addictive habit reinforced by the relief from craving produced by the act of purchasing particular products. In our Taiwanese but not in our British sample, compulsive shopping led to hoarding behavior. This implies that the Taiwanese are more reluctant to discard items that they do not use. We discuss cultural factors that may account for this finding

Digital identity : the effect of trust and reputation information on user judgement in the sharing economy

The Sharing Economy (SE) is a growing ecosystem focusing on peer-to-peer enterprise. In the SE the information available to assist individuals (users) in making decisions focuses predominantly on community generated trust and reputation information. However, how such information impacts user judgement is still being understood. To explore such effects, we constructed an artificial SE accommodation platform where we varied the elements related to hosts' digital identity, measuring users' perceptions and decisions to interact. Across three studies, we find that trust and reputation information increases not only the users' perceived trustworthiness, credibility, and sociability of hosts, but also the propensity to rent a private room in their home. This effect is seen when providing users both with complete profiles and profiles with partial user-selected information. Closer investigations reveal that three elements relating to the host's digital identity are sufficient to produce such positive perceptions and increased rental decisions, regardless of which three elements are presented. Our findings have relevant implications for human judgment and privacy in the SE, and question its current culture of ever increasing information-sharing

Judgments in the sharing economy: the effect of user-generated trust and reputation information on decision-making accuracy and bias

The growing ecosystem of peer-to-peer enterprise – the Sharing Economy (SE) – has brought with it a substantial change in how we access and provide goods and services. Within the SE, individuals make decisions based mainly on user-generated trust and reputation information (TRI). Recent research indicates that the use of such information tends to produce a positivity bias in the perceived trustworthiness of fellow users. Across two experimental studies performed on an artificial SE accommodation platform, we test whether users’ judgments can be accurate when presented with diagnostic information relating to the quality of the profiles they see or if these overly positive perceptions persist. In study 1, we find that users are quite accurate overall (70%) at determining the quality of a profile, both when presented with full profiles or with profiles where they selected three TRI elements they considered useful for their decision-making. However, users tended to exhibit an “upward quality bias” when making errors. In study 2, we leveraged patterns of frequently vs. infrequently selected TRI elements to understand whether users have insights into which are more diagnostic and find that presenting frequently selected TRI elements improved users’ accuracy. Overall, our studies demonstrate that – positivity bias notwithstanding – users can be remarkably accurate in their online SE judgments