Cause-Brand Community: Toward Consumer Citizenship

Brand communities have been proven to be successful with commercial brands. However, a review of the literature indicated that brand community has not been studied in charity organizations. To shed light on this issue, a qualitative research was conducted among stakeholders of an internationally well-known charity organization using three different techniques: in-depth interviews, netnography, and nonparticipant observation at the charity’s brandfest. The results suggest that cause-brand communities exist, sharing the same elements of commercial-brand communities, but the two have some unique characteristics, such as the existence of different stakeholders and small communities that demonstrate an extreme commitment, the promotion of consumer citizenship among them, and the lack of oppositional brand loyalty. A cause-brand community has important managerial implications as it helps build the charity’s brand equity; at the same time, a well-known brand supports the construction of the brand community.     

TOOLS FOR RE‐RANDOMIZATION

With the rising popularity of field experiments in economics, re‐randomization schemes have emerged as tools to induce balance in observable variables across treatment groups. However, re‐randomization is not fully understood and the methodologies to estimate its effects on the distribution of parameters are still under‐developed. This paper helps to close that gap by suggesting an asymptotically normal re‐randomization scheme and bootstrapping procedure to carry inference under a wide range of estimators and re‐randomization schemes.     

Using clustering ensemble to identify banking business models

The business models of banks are often seen as the result of a variety of simultaneously determined managerial choices, such as those regarding the types of activities, funding sources, level of diversification, and size. Moreover, owing to the fuzziness of data and the possibility that some banks may combine features of different business models, the use of hard clustering methods has often led to poorly identified business models. In this paper we propose a framework to deal with these challenges based on an ensemble of three unsupervised clustering methods to identify banking business models: fuzzy c‐means (which allows us to handle fuzzy clustering), self‐organizing maps (which yield intuitive visual representations of the clusters), and partitioning around medoids (which circumvents the presence of data outliers). We set up our analysis in the context of the European banking sector, which has seen its regulators increasingly focused on examining the business models of supervised entities in the aftermath of the twin financial crises. In our empirical application, we find evidence of four distinct banking business models and further distinguish between banks with a clearly defined business model (core banks) and others (non‐core banks), as well as banks with a stable business model over time (persistent banks) and others (non‐persistent banks). Our proposed framework performs well under several robustness checks related with the sample, clustering methods, and variables used.     

The effects of social media on brand attitude and WOM during a brand crisis: Evidences from the Barilla case

Brand crises endanger companies. Social media are a key platform where stakeholders elaborate and react to crises. This research explores whether social media audience and traditional mass media audience react differently to a brand crisis in terms of their (1) attitude towards the brand and (2) word-of-mouth (WOM). As to the brand attitude, we argue that stakeholders mainly exposed to the brand crisis via social media have a more negative reaction towards the brand compared to those who are principally exposed via traditional media. As to the behaviour, we posit that social media exposure intensifies WOM. By analysing the Barilla crisis, the findings suggest that the social media act as ‘multipliers’ of the reaction of stakeholders to a brand crisis.     

BidPacket: trading bandwidth in public spaces

Smart devices such as smartphones and tablets are used extensively in public spaces for the transmission and reception of content in the form of text, photos and streaming videos. Since the bandwidth provided for wireless access is limited in public areas, it becomes an issue for users to gain access to the bandwidth they need at the right times. While an omniscient controller could assign bandwidth to each device on the basis of their needs and overall availability, imperfect information about the instantaneous state of the wifi access patterns and needs of users make for a very inefficient allocation of such bandwidth. This paper provides a solution for bandwidth allocation by creating a market among users of smart devices so that they can bid for extra bandwidth when they need it and sell it when they don’t. They do so by using a virtual currency that is conserved so that each device owner maximizes his own utility. This utility function is composed of both the benefit accrued from accessing bandwidth and the loss of the currency incurred in bidding for such bandwidth. Extensive simulations show that this market-based method outperforms an omniscient model when demand is uncertain, while minimizing bandwidth consumption.     

Collaboration,motivation, and the size of organizations

We present an analytical model of an organization that offers operational drivers of limits on team size. The model trades off benefits from collaborative problem solving against the disadvantages of diminishing motivation when groups get large. Collaboration is represented as parallel employee activity combined with frequent sharing of partial information, with a resulting superlinear performance increase over team size. Motivation is modeled by team members periodically setting an effort level either to contribute to the best of their ability or to “cruise”; at the minimum level not recognizable as shirking. Each individual decision is limited by bounded rationality based on team rewards, the time horizon of team interaction, and individual expectations about colleagues’ behavior. The decision collapses to a simple “barrier rule”;. Work hard when a certain “barrier percentage”; of team members work hard, and otherwise shirk. The influence of team size on this barrier percentage depends on the extent of benefits from collaboration: As long as performance increases quadratically with team size, the increased benefits resulting from collaboration exactly balance the temptation to shirk, with the barrier percentage approaching a fixed limit for large team sizes. As soon as the performance increase slows to anything less than quadratic, shirking eventually sets in and limits the possible size of the team. This implies that cooperation is sustainable in large organizational units, provided the problem‐solving processes used are powerful enough to ensure sufficient performance increases. Thus, effective problem‐solving methods are of double value, improving direct productivity and mitigating the social dilemma from team production. A manager should enlarge his or her organization up to the minimum of the limit set by the cooperation barrier and the exogenous performance limit.     

Pigouvian Taxes: A Strategic Approach

This paper analyzes the problem of designing mechanisms to implement efficient solutions in economies with externalities. We provide two simple mechanisms implementing the Pigouvian Social Choice Correspondence in environments in which coalitions can or cannot be formed.     

The Breakdown of Idiosyncratic Volatility Into Expected and Unexpected Components and Its Effects on Stock Returns in Brazil

Based on studies of idiosyncratic volatility developed in the recent literature, this study analyzes its relation with expected returns through the breakdown of idiosyncratic volatility in the Brazilian stock market and presents evidence of the importance of expected idiosyncratic volatility for asset pricing. We study the impact of the expected and unexpected components of idiosyncratic volatility on the returns of shares listed on the BOVESPA between 2004 and 2011. The results show a strong positive and significant relation between expected idiosyncratic volatility and returns. This evidence is highlighted when we use unexpected idiosyncratic volatility to control for unexpected returns. Additional robustness tests, controlling for size and momentum effects, also have positive and significant coefficients, corroborating previous findings.     

Intolerable nuisances: some laboratory evidence on survivor curve shapes

The fraction of a user population willing to tolerate nuisances of size x is summarized in the survivor curve S(x); its shape is crucial in economic decisions such as pricing and advertising. We report a laboratory experiment that, for the first time, estimates the shape of survivor curves in several different settings. Laboratory subjects engage in a series of six desirable activities, e.g., playing a video game, viewing a chosen video clip, or earning money by answering questions. For each activity and each subject we introduce a chosen level \(x \in [x_{\min }, x_{\max }]\) of a particular nuisance, and the subject chooses whether to tolerate the nuisance or to switch to a bland activity for the remaining time. New non-parametric techniques provide bounds on the empirical survivor curves for each activity. Parametric fits of the classic Weibull distribution provide estimates of the survivor curves’ shapes. The fitted shape parameter depends on the activity and nuisance, but overall the estimated survivor curves tend to be log-convex. An implication, given the model of Aperjis and Huberman (SSRN, doi:  10.2139/ssrn.1672820, 2011), is that introducing nuisances all at once will generally be more profitable than introducing them gradually.