State‐Dependent Forward Guidance and the Problem of Inconsistent Announcements

Forward guidance can be provided as an unconditional promise, i.e. commitment to a specific low policy rate. Alternatively, the promise may include an escape clause, i.e. a condition defining the state of the economy under which the central bank would not keep such a low rate and, instead, it would revert to setting policy under discretion. The escape clause can be expressed as a threshold in terms of a specific variable. The present paper shows that, when such a threshold is expressed in terms of an endogenous variable (e.g. output, inflation), there are cases where it becomes impossible for the central bank to act in a way that is consistent with its promise. Consistency imposes limits on the policy rate that can be set since reverting immediately to the optimal discretionary rate can be incompatible with exceeding the threshold.     

An Ethical Inquiry of the Effect of Cockpit Automation on the Responsibilities of Airline Pilots: Dissonance or Meaningful Control?

Journal of Business Ethics - Airline pilots are attributed ultimate responsibility and final authority over their aircraft to ensure the safety and well-being of all its occupants. Yet, with the...     

Time series copulas for heteroskedastic data

We propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first‐order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co‐movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value‐at‐risk forecasts.     

Building relationship innovation in global collaborative partnerships: big data analytics and traditional organizational powers

This study examines how relationship innovation can be developed in global collaborative partnerships (alliances, joint ventures, mergers, and acquisitions). The recently emerging theory of big data analytics linked with traditional organizational powers has attracted a growing interest, but surprisingly little research has been devoted to this important and complex topic. Therefore, after developing the theoretical foundations, our study empirically quantifies the links between the theoretical constructs based on the data collected from chief executive officers, managing directors, and heads of departments who work in contemporary global data‐and‐information driven collaborative partnerships. The results from structural equation modeling indicate that the relationship innovation depends on the power of big data analytics and non‐mediated powers (NMP, expert and referent). The power of big data analytics also mediates the correlation between NMP and relationship innovation. However, mediated powers (coercive and manipulative) negatively affect the power of big data analytics and relationship innovation. The interaction effects further depict that analytically powered partnerships have better relationship innovation compared with those which focus less on the analytical power. Consequently, the contributions of this study provide a deeper understanding of mechanisms of how modern collaborative partnerships can use big data analytics and traditional organizational powers to co‐create relationship innovation.     

Reciprocal value sharing in manufacturer-retailer relationships: the case of new product introductions

Prior literature examined reciprocity in the context of value creation. However, research has yet to examine whether reciprocity exists in value sharing. To address this gap, the authors examine retailer’s reciprocal value sharing with its manufacturer in relation to new product introductions. The authors test, via a survey of retail managers, whether reciprocal value sharing is influenced by an interaction of manufacturer’s prior new product success with innovativeness of a manufacturer’s products and the frequency of new product introduction. The results indicate that a retailer’s reciprocal value sharing is greater when the manufacturer historically launched successful new products, and that this effect is decreased with the innovativeness of a manufacturer’s products but increased with the frequency of new product introduction.     

Gamified interactions: whether,when, and how games facilitate self–brand connections

Firms increasingly use games to interact with their customers. Yet, surprisingly little is known about whether, when, and how such “gamified” interactions engage consumers with a firm’s brand, thereby facilitating self–brand connections. Building on flow theory, we show that gamified interactions that are highly interactive and optimally challenging facilitate self–brand connections, because such games lead to emotional and cognitive brand engagement. A field study and three experiments across various product domains and game designs support our theory. We also identify conditions under which consumers do not become engaged with a brand, namely when firms restrict their decisional control either to voluntarily participate in the game (i.e., compulsory play) or to spend as much time as desired playing the game (i.e., time pressure). Our findings advance existing knowledge about the use of games in marketing and provide important implications for how marketers can harness their potential to build self–brand connections.     

EMPIRICAL PERFORMANCE OF GARCH MODELS WITH HEAVY‐TAILED INNOVATIONS

We introduce a new type of heavy‐tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), to the GARCH and Glosten‐Jagannathan‐Runkle (1993) GARCH models, and compare its empirical performance with two other popular types of heavy‐tailed distribution, the Student's t distribution and the normal inverse Gaussian distribution (NIG), using a variety of asset return series. Our results illustrate that there is no overwhelmingly dominant distribution in fitting the data under the GARCH framework, although the NRIG distribution performs slightly better than the other two types of distribution. For market indexes series, it is important to introduce both GJR‐terms and the NRIG distribution to improve the models’ performance, but it is ambiguous for individual stock prices series. Our results also show the GJR‐GARCH NRIG model has practical advantages in quantitative risk management. Finally, the convergence of numerical solutions in maximum‐likelihood estimation of GARCH and GJR‐GARCH models with the three types of heavy‐tailed distribution is investigated.     

Human resource management practices and voluntary turnover: a study of internal workforce and external labor market contingencies

We tested relationships between employee quit rates and two bundles of human resource (HR) practices that reflect the different interests of the two parties involved in the employment relationship. To understand the boundary conditions for these effects, we examined an external contingency proposed to influence the exchange-based effects of HR practices on subsequent quit rates – the local industry-specific unemployment rate – and an internal contingency proposed to shape employees’ conceptualization of their exchange relationship – their employment status (i.e. full-time, part-time and temporary employment). Analyses of lagged data from over 200 Canadian establishments show that inducement HR practices (e.g. extensive benefits) and performance expectation HR practices (e.g. performance-based bonuses) had different effects on quit rates, and the former effect was moderated by unemployment rate. The effects of HR practices on quit rates did not differ between FT and PT employees, but a different pattern of main and interactive effects was found among temporary workers. These findings suggest that employees’ exchange-based decisions to leave may be less affected by the number of hours they expect to work each week, and more by the number of weeks they expect to work.     

Entrepreneurship and knowledge spillovers from the public sector

A compelling body of research has found that investments in knowledge from other firms and universities spill over to enhance the performance of entrepreneurial firms. This literature has shown that firm performance is positively related to investments in new knowledge by other firms and research universities. This paper addresses a gap in the literature by positing that public sector knowledge is also conducive to enhancing performance by knowledge intensive entrepreneurial (KIE) firms. Our findings suggest that the public sector provides a fertile source of knowledge for enhancing KIE firm performance.