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Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

Since illiquidity risk is one of the most important pricing factors of assets, the aim of this paper is to evaluate the suitability of proxies of illiquidity prevalent in the asset pricing literature and their explanatory power in asset pricing tests.

Using the available high‐frequency intra‐day data, the paper constructs some proxies of illiquidity as benchmarks and then evaluates proxies of illiquidity based on inter‐day data.

The empirical results provide convincing evidence that turnover is the most suitable proxy of illiquidity in the Chinese stock market. It is not only hghly related to intra‐day data‐based proxies of illiquidity but also completely superior to other measures of illiquidity in asset pricing tests.

First, the paper applies illiquidity measurements from microstructure theory and the available high‐frequency data, and examines the suitability of illiquidity proxies in asset pricing literature in the Chinese stock market. Rational basics are provided to test the applicability of illiquidity measures in the Chinese stock market. Second, the paper introduces illiquidity proxies into asset pricing models to extend their explanatory power. The paper's results may help researchers to select illiquidity proxies more cautiously.

Real estate is the last major asset class without liquid derivatives markets. The reasons for that are not fully known or understood. Therefore, the purpose of this paper is to better understand the main factors that influence the propensity of commercial real estate investors in the UK to employ property derivatives.

The research methodology that was chosen for this research is grounded theory which, in its original form, goes back to Glaser and Strauss (1967). A total of 43 interviews were conducted with 46 real estate professionals in the UK from property investment management firms (investing directly or indirectly in real estate), multi-asset management firms, real estate investment trusts, banks, and brokerage and advisory firms, among others.

The research results show 29 factors that influence the propensity of direct and indirect real estate investors in the UK to employ property derivatives. Out of the 29 factors, the current research identified 12 factors with high-explanatory power, 6 with a contributing role and 11 with low explanatory power. Moreover, factors previously discussed in the literature are tested and assessed as to their explanatory power. The focus of this paper is on those factors with high-explanatory power. From the research data, three main reasons have been identified as the sources of investor reluctance to trade in property derivatives. The first and main reason is related to a mismatch between motivations of property investment managers and what can be achieved with the instruments. The second reason, which ties in with the first one, is a general misunderstanding as to the right pricing technique of property derivatives. Finally, the third reason is a general lack of hedging demand from the investor base owing to the long investment horizons through market cycles.

The research contributes to the literature on property derivatives in various ways. First, it extends the literature on market hurdles in property derivatives markets by testing and extending the hurdles that were proposed previously. Second, the research shows that the existing pricing models need to be extended in order to account for the risk perception of practitioners and their concerns with regard to liquidity levels.

For both theory and practice, the research has shown some limitations in using property derivatives for purposes such as creating index exposure or hedging. Another contribution, in this case to practice, is that this study provides a clearer picture as to the reasons that keep property investment managers away from using property derivatives.

The research results indicate that liquidity per se is not a universal remedy for the problems in the market. In addition to the need for improving the understanding of the pricing mechanism, practitioners should give more thought to the notion of real estate market risk and the commensurate returns that can reasonably be expected when they take or reduce it. This implies that property index futures currently do not price like those on any other investable asset class.

The purpose of this eight-country study is to examine what drives performance at the individual worker’s level and compare the explanatory power of such drivers between emerging, newly developed and developed markets around the globe.

The study combines established behavioural theory developed in a Western context with three factors anticipated to be most relevant in Asia (competitive attitude, willingness to serve and speed) as drivers of workforce performance. Four thousand working and middle-class respondents from eight countries were sampled. The associations were tested using structural equation modelling, and workforce performance was measured using univariate analysis.

Three country clusters emerged from the research: emerging economies in Asia (Indonesia, India), where the three factors powerfully explain performance; “Confucian orbit countries” (China, Japan, Korea), where the factors explain 81-93 per cent; and highly developed Western countries (the USA, the UK, Germany), where the factors account for only 20-29 per cent.

As well as providing a framework for modelling workforce performance, particularly in Asian countries, the findings indicate that workforce performance should be incorporated in performance indexes. The findings as to which drivers best explain workforce performance in each country can inform workforce recruitment and management, as well as the location of businesses and outsourcing.

For the first time, the study addresses the anomaly between economic growth and development experienced by Asian countries and their relatively low rankings in global competitiveness indexes by making the link between workforce performance and country performance.

The purpose of this paper is to investigate how and why a firm’s product market power affects its dividend policy.

This paper uses three measures of market power? The degree of import competition, Herfindahl-Hirschman index, and Lerner Index? To examine how a firm’s product market power affects its dividend policy. Further, it proposes and tests a risk-based explanation for this impact.

This paper shows that market power positively affects the dividend decision, in terms of both the probability of paying a dividend and the amount of dividend payment. It also provides evidence that the route through which market power affects the dividend decision is business risk: firms with less market power are riskier and hence less likely to pay dividends than firms with more market power.

The results show that product market power may have played an important role in reshaping dividend policy of corporate America.

This study documents the relevance of market power behind dividend policy and therefore adds to the knowledge on the relationship between product markets and corporate financial policies, which is an important and understudied area of corporate finance.

Power distance describes a central facet of national culture, because it influences the acceptance and endorsement of job characteristics related to status and power. This has major implications for international human resource management, because the importance of different situational job characteristics for employee job satisfaction should differ across cultures. The purpose of this paper is to analyse if and how national power distance levels moderate different situational job characteristics’ influence on job satisfaction.

The authors refer to three approaches to culture: the frameworks of Hofstede and GLOBE as well as to current scores provided in a meta-analysis. The empirical findings are derived using regression analyses on a sample covering 16 nations.

The results are convincing regarding the basic job satisfaction driver model not involving culture. However, the results on power distance’s impact as well as its moderating role are strongly dependent on the culture concepts utilised. The authors provide an analysis of differences along the measurements behind the different concepts.

The authors can conclude that national differences in job satisfaction, as found in various studies, are a result of differences in situational dispositions to work life rather than a result of different cultural surroundings in terms of power distance. The question is whether this is due to power distance’s lack of impact or due to other factors, such as the difficulties of measuring culture. The authors discuss the differences which are due to different measurements. For ultimately confirming power distance’s moderating role and for advancing theorizing in this field, further research, which can build on the framework offered in this paper, is needed that directly measures the individual power distance facets in addition to the job characteristics and satisfaction values.

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

This paper aims to explore the relationship between bank market power and stability of financial institutions in Italy between 2001 and 2012. The authors first test the existence of a U-shaped relationship between market power and financial stability. Second, they regress the market share indicator on bank risk-taking to underline whether financial stability is affected by increasing or decreasing the market power of banks. Third, they explore whether this relationship is affected by the size, level of capitalization and credit insolvency of banks.

Relying on highly territorially disaggregated data at labor market areas level, the authors estimate the impact of bank market power and other explanatory variables on a proxy of risk taking behavior such as the banking “stability inefficiency” derived simultaneously from the estimation of a stability stochastic frontier. Bank market power is taken into account through an individual measure based on loans. Financial stability is calculated through the Z-score. The authors use, as risk-taking measure, the stability inefficiency whose estimation approach is the stochastic frontier analysis.

The empirical evidence shows that the inefficiency of financial stability is found to be U-shaped related with respect to the measure of market power. Bank size is an essential factor in explaining the relationship between bank market power and risk-taking. Cooperative banks have fewer incentives to gain market power to better perform in term of risks. The reform of the cooperative banks that took recently place in Italy is not supported by the data.

The relationship between bank market power and financial stability has been analyzed using a rich sample of cooperative, commercial and popular banks in Italy over the 2001-2012 period. The authors rely on labor market areas being sub-regional geographical areas where the bulk of the labor force lives and works. The paper investigates the market power-stability link considering both cooperative and non-cooperative banks. Indeed, specific attention has been paid on cooperative banks because of their mission in favor of the local community as only few studies, to the best of the authors’ knowledge, examine cooperative banking.

Previous empirical research interprets results from pay‐performance studies in the light of either agency theory or managerial power theory. This paper aims to directly estimate the relationship between CEO power, and compensation structure, level, and performance‐sensitivity. In doing so, it seeks to test the crucial assumption in managerial power theory according to which more powerful CEOs are able to enjoy higher and less performance‐sensitive compensation.

The hypotheses are tested on a detailed dataset, covering compensation for CEOs in virtually all Dutch stock‐listed companies, for the period 2002‐2006. The paper tests whether the findings are robust against different lag structures and firm size classes.

In general, most of the multi‐dimensional measures of power do not appear to have a strong effect on compensation, with one exception: non‐Dutch CEOs receive more variable compensation, and receive higher and less performance‐sensitive pay than their Dutch colleagues.

This paper contributes to the extant CEO compensation literature, which to date relies on interpretations of findings in pay‐for‐performance studies to argue for either agency or managerial power theory. The direct test of the relationship between power and compensation emphasises the importance of one dimension of a multidimensional power construct. As strong effects of performance of compensation are not found either, the paper suggests that the bipolar debate be extended to include other explanations of compensation arrangements.

The purpose of this paper is to consolidate existing knowledge and provide a deeper understanding of the use of social media (SM) data for predictions in various areas, such as disease outbreaks, product sales, stock market volatility and elections outcome predictions.

The scientific literature was systematically reviewed to identify relevant empirical studies. These studies were analysed and synthesized in the form of a proposed conceptual framework, which was thereafter applied to further analyse this literature, hence gaining new insights into the field.

The proposed framework reveals that all relevant studies can be decomposed into a small number of steps, and different approaches can be followed in each step. The application of the framework resulted in interesting findings. For example, most studies support SM predictive power, however, more than one-third of these studies infer predictive power without employing predictive analytics. In addition, analysis suggests that there is a clear need for more advanced sentiment analysis methods as well as methods for identifying search terms for collection and filtering of raw SM data.

The proposed framework enables researchers to classify and evaluate existing studies, to design scientifically rigorous new studies and to identify the field's weaknesses, hence proposing future research directions.