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One challenge for tourism recommendation systems (TRSs) is the long-tail phenomenon of ratings or popularity among tourist products. This paper aims to improve the diversity and efficiency of TRSs utilizing the power-law distribution of long-tail data.

Using Sina Weibo check-in data for example, this paper demonstrates that the long-tail phenomenon exists in user travel behaviors and fits the long-tail travel data with power-law distribution. To solve data sparsity in the long-tail part and increase recommendation diversity of TRSs, the paper proposes a collaborative filtering (CF) recommendation algorithm combining with power-law distribution. Furthermore, by combining power-law distribution with locality sensitive hashing (LSH), the paper optimizes user similarity calculation to improve the calculation efficiency of TRSs.

The comparison experiments show that the proposed algorithm greatly improves the recommendation diversity and calculation efficiency while maintaining high precision and recall of recommendation, providing basis for further dynamic recommendation.

TRSs provide a better solution to the problem of information overload in the tourism field. However, based on the historical travel data over the whole population, most current TRSs tend to recommend hot and similar spots to users, lacking in diversity and failing to provide personalized recommendations. Meanwhile, the large high-dimensional sparse data in online social networks (OSNs) brings huge computational cost when calculating user similarity with traditional CF algorithms. In this paper, by integrating the power-law distribution of travel data and tourism recommendation technology, the authors’ work solves the problem existing in traditional TRSs that recommendation results are overly narrow and lack in serendipity, and provides users with a wider range of choices and hence improves user experience in TRSs. Meanwhile, utilizing locality sensitive hash functions, the authors’ work hashes users from high-dimensional vectors to one-dimensional integers and maps similar users into the same buckets, which realizes fast nearest neighbors search in high-dimensional space and solves the extreme sparsity problem of high dimensional travel data. Furthermore, applying the hashing results to user similarity calculation, the paper greatly reduces computational complexity and improves calculation efficiency of TRSs, which reduces the system load and enables TRSs to provide effective and timely recommendations for users.

The purpose of this study was to examine the extent to which chief executive officers (CEOs) deserve the pay they receive both in terms of over and underpayment.

Rather than using the traditional normal distribution view in which CEO performance clusters around the mean with relatively little variance, the authors adopt a novel power law approach. They studied 22 industries and N = 4,158 CEO-firm combinations for analyses based on Tobin’s Q and N = 5,091 for analyses based on return on assets. Regarding compensation, they measured the CEO distribution based on total compensation and three components of CEO total pay: salary, bonus, and value of options exercised.

In total, 86 percent of CEO performance and 91 percent of CEO pay distributions fit a power law better than a normal distribution, indicating that a minority of CEOs are producing top value for their firms (i.e. CEO performance) and a minority of CEOs are appropriating top value for themselves (i.e. CEO pay). But, the authors also found little overlap between CEOs who are the top performers and CEOs who are the top earners.

The findings shed new light on CEO pay deservingness by using a novel conceptual and methodological lens that highlights systematic over and underpayment. Results suggest a violation of distributive justice and offer little support for agency theory’s efficient contracting hypothesis, which have important implications for agency theory, equity theory, justice theory, and agent risk sharing and agent risk bearing theories.

Results highlight erroneous practices when trying to benchmark CEO pay based on average levels of performance in an industry because the typical approach to CEO compensation based on averages significantly underpays stars and overpays average performers.

Results offer new insights on the extent of over and underpayment. The findings uncover an extremely large non-overlap between the top earning and top performing CEOs and to an extent far greater in magnitude than previously suggested.

Objetivo – El objetivo de nuestro estudio fue examinar si los directores ejecutivos (CEOs) merecen la remuneración monetaria que reciben.

Metodología – En lugar de utilizar el enfoque tradicional que asume que la distribución del rendimiento de CEOs sigue la curva normal (con la mayoría de CEOs agrupados en torno a la media y relativamente poca variación), adoptamos un enfoque diferente basado en la ley de potencia. Incluimos 22 industrias y N = 4.158 combinaciones de CEO-firma para análisis basados en Tobin’s Q y N = 5.091 para análisis basado en la rentabilidad de los activos. En cuanto a la remuneracion, medimos distribuciones basadas en la remuneración total y tres componentes del pago completo a los CEOs: salario, bonos, y el valor de las opciones ejercitadas.

Resultados – 86% de las distribuciones de rendimiento de CEOs y el 91% de las distribuciones de pago de los CEO se aproximan mejor a una distribución de ley de potencia que a una distribución normal. Esto indica que una minoría de los CEOs produce un valor muy superior para sus empresas (es decir, el rendimiento CEO) y una minoría de los CEOs apropia valor superior para sí mismos (es decir, pago de los CEO). Sin embargo, encontramos muy poco solapamiento entre aquellos CEOs que se desempeñan mejor y los CEOs que ganan más.

Implicaciones – Nuestros hallazgos usando una conceptualización y metodología novedosas ponen en relieve que a muchos CEOs se les paga demasiado y que a muchos no se les paga suficiente (en comparación con su desempeño). Los resultados sugieren una violación de los principios de justicia distributiva y no apoyan la hipótesis de “contratación eficiente,” y tienen implicaciones para para la teoría de la agencia, de la equidad, de la justicia, y de la distribución de riesgos.

Implicaciones prácticas – Los resultados destacan las prácticas erróneas con respecto a la distribución de compensación a CEOs que se basan en los niveles medios de rendimiento en una industria. Estas prácticas llevan a no pagar suficiente a los directivos “estrella” y pagar demasiado a los directivos con desempeño medio.

Originalidad/valor – Los resultados ofrecen nuevas perspectivas sobre la relación entre desempeño y compensación de CEOs y que los que se desempeñan mejor no son los que reciben más pago, y viceversa. Estas diferencias son mucho más grandes de que lo que se creía anteriormente.

Objetivo – O objetivo do nosso estudo foi examinar se os CEOs merecem a compensação monetária que recebem.

Metodologia – Em vez de utilizar a abordagem tradicional que assume que a distribuição do desempenho do CEO segue a curva normal (com a maioria dos CEOs agrupados em torno da média e relativamente pouca variação), adotamos uma abordagem diferente com base num enfoque inovador da lei de potência. Incluímos 22 indústrias e N = 4.158 combinações de CEO-empresa para análise baseada no Q de Tobin e N = 5091 para análise baseado na rentabilidade dos ativos. Em relação à compensação, medimos as distribuições de CEO com base no total de compensação e três componentes do pagamento total dos CEOs: salário, bônus e o valor das opções exercidas.

Resultados – 86% do desempenho do CEO e 91% das distribuições de pagamento do CEO correspondem a uma lei de potência melhor do que uma distribuição normal, indicando que uma minoria de CEOs está produzindo valor superior para suas empresas (ou seja, desempenho do CEO) e uma minoria de CEOs se apropriando do valor superior para si próprios (isto é, o salário do CEO). Mas, também encontramos pouca sobreposição entre CEOs que tem os melhores desempenhos e os CEOs que tem as maiores ganancias.

Implicações – Nossas descobertas lançam nova luz sobre o merecimento do pagamento do CEO, usando uma nova lente conceitual e metodológica que destaca o excessivo e o baixo pagamento sistemático. Os resultados sugerem uma violação da justiça distributiva e não apoiam a hipótese da contratação eficiente, e tem implicações para a teoria da agência, teoria da igualdade, teoria da justiça e distribuição de riscos.

Implicações práticas – Os resultados destacam práticas errôneas quando se tenta benchmark de remuneração do CEO baseado em níveis médios de desempenho em uma indústria, porque essas práticas levam a não pagar o suficiente aos CEOs “estrela” e pagar em excesso CEOs com desempenho médio.

Originalidade/valor – Os resultados oferecem novas perspectivas sobre a relação entre desempenho e retribuição dos CEOs e que os que desempenham melhor não são os que recebem um pagamento maior, e vice-versa. Estas diferenças são muito maiores do que se pensava anteriormente.

The purpose of this paper is to reveal the multi‐scale relation between power law distribution and correlation of stock returns and to figure out the determinants underlying capital markets.

The multi‐scale relation between power law distribution and correlation is investigated by comparing the original series with the special series. The eliminating intraday trend series approach developed by Liu et al. is utilized to analyze the effects of power law decay change on correlation properties, and shuffling series originated by Viswanathan et al. for the impacts of special type of correlation on power‐law distribution.

It is found that the accelerating decay of power law has an insignificant effect on correlation properties of returns and the empirical results indicate that time scale may also be an important factor maintaining power law property of returns besides correlation. When time scale is under critical point, the effects of correlation are crucial, and the correlation of nonlinear long‐range presents the strongest influence. However, for time scale beyond critical point, the impact of correlation begins to diminish or even finally disappear and then the power law property shows complete dependence on time scale.

The 5‐min high frequency data of the Shanghai market as the empirical benchmark is insufficient to depict the relation over the entire time scale in the Chinese stock market.

The paper identifies the determinants of market dynamics to apply them to risk management through analysis of multi‐scale relations, and supports endeavors to introduce time parameter into further risk measures and control.

The paper provides the empirical evidence that time scale is one of the key determinants of market dynamics by analyzing the multi‐scale relation between power law distribution and correlation.

The potential advantage of extreme value theory in modeling management phenomena is the central theme of this paper. The statistics of extremes have played only a very limited role in management studies despite the disproportionate emphasis on unusual events in the world of managers. An overview of this theory and related statistical models is presented, and illustrative empirical examples provided.

The purpose of this paper is to propose a method for statistical inference on data from power law distributions in order to explain behavior and social phenomena associated with web‐based social spaces such as discussion forums, question‐and‐answer sites, web 2.0 applications and the like.

The paper starts by highlighting the importance of explaining behavior in social networks. Next, the power law nature of social interactions is described and a hypothetical example is used to explain why analyzing sub‐sets of data might misrepresent the relationship between variables having power law distributions. Analysis requires the use of the complete distribution. The paper proposes logarithmic transformation prior to correlation and regression analysis and shows why it works using the hypothetical example and field data retrieved from Microsoft's Netscan project.

The hypothetical example emphasizes the importance of analyzing complete datasets harvested from social spaces. The Netscan example shows the importance of the logarithmic transformation for enabling the development of a predictive regression model based on the power law distributed data. Specifically, it shows that the number of new and returning participants are the main predictors of discussion forum activity.

This paper offers a useful analysis tool for anyone interested in social aspects of the Internet as well as corporate intra‐net systems, knowledge management systems or other systems that support social interaction such as cellular phones and mobile devices. It also explains how to avoid errors by paying attention to assumptions and range restriction issues.

Mergers and acquisitions (M&As) typically accelerate target top management team (TMT) executive departures. Market discipline and Relative Standing are two major and competing economic and sociological explanations for this phenomenon which lack a satisfactory theoretical integration. To fill this gap in the literature, we model the M&A market as a complex adaptive system composed of TMTs which rid themselves of executives via self-organized critical processes, generating M&A market-level properties that are emergent, or not easily explained with reference to the individual TMTs. The observation of an emergent power law distribution in target TMT executive retention rates for M&A activities in the United States supports our interpretation.

Fat‐tailed distributions have been found in many financial and economic variables ranging from forecasting returns on financial assets to modeling recovery distributions in bankruptcies. They have also been found in numerous insurance applications such as catastrophic insurance claims and in value‐at‐risk measures employed by risk managers. Financial applications include:

Modern property investment allocation techniques are typically based on recognised measures of return and risk. Whilst these models work well in theory under stable conditions, they can fail when stable assumptions cease to hold and extreme volatility occurs. This is evident in commercial property markets which can experience extended stable periods followed by large concentrated negative price fluctuations as a result of major unpredictable events. This extreme volatility may not be fully reflected in traditional risk calculations and can lead to ruin. The paper aims to discuss these issues.

This research studies 28 years of quarterly Australian direct commercial property market performance data for normal distribution features and signs of extreme downside risk. For the extreme values, Power Law distribution models were examined as to provide a better probability measure of large negative price fluctuations.

The results show that the normal bell curve distribution underestimated actual extreme values both by frequency and extent, being by at least 30 per cent for the outermost data point. For the statistical outliers beyond 2 SD, a Power Law distribution can overcome many of the shortcomings of the standard deviation approach and therefore better measure the probability of ruin, being extreme downside risk.

In highlighting the challenges to measuring property market performance, analysis of extreme downside risk should be separated from traditional standard deviation risk calculations. In recognising these two different types of risk, extreme downside risk has a magnified domino effect with the tendency of bad news to come in crowds. Big price changes can lead to market crashes and financial ruin which is well beyond the standard deviation risk measure. This needs to be recognised and developed as there is evidence that extreme downside risk determinants are increasing by magnitude, frequency and impact.

Analysis of extreme downside risk should form a key part of the property decision process and be included in the property investment manager’s toolkit. Modelling techniques for estimating measures of tail risk provide challenges and have shown to be beyond traditional risk management practices, being too narrow and constraining a definition. Measuring extreme risk and the likelihood of ruin is the first step in analysing and dealing with risk in both an asset class and portfolio context.

The purpose of this study is to examine the growth patterns of tag vocabulary in collaborative tagging systems to verify the sustainability and stabilization of tag distributions. Both sustainability and stabilization are essential to the mining and categorization of information driven by tagging behaviors.

The study was based on time series data of CiteULike from November 2004 to April 2010. Power law distributions were detected to reveal statistical regularities and tagging patterns. Logistic regression analysis with time‐dependent covariates was conducted to identify the factors affecting the growth of distinct tags for articles. The significance of the effects and the time taken for a given article to reach its tagging maturity were also explored.

Time series plots and trend analysis illustrated the continuous growth of the tagging system. Exploratory analysis of power law distribution fittings indicated a sign of system stability known as scale invariance. Logistic regression results demonstrated that for a particular article, the number of users who tagged the article, the initial date when the article was tagged, and the life span of the article are statistically significant to the ratio of the distinct tag number to the total tag number for a given article. These results confirmed that the distinct tag ratio of an article gives rise to a stable pattern.

Though extensive work has been done on the patterns of tag vocabulary, it is not clear how the growth of distinctive tags behaves in relation to the total number of tag applications, considering time‐dependent covariates such as the number of users, and the longevity of an article. This paper sets to complement the literature on the existing methodology and investigate this property in detail.