All work intensity is not created equal: Effort motives,job satisfaction and quit intentions at a grocery chain

Prior research has shown that the well-being of employees engaged in intensive work can vary with the discretion their jobs afford regarding how and when to carry out the work. This article explores a different avenue. It argues that well-being also varies with employees’ individual motives for working intensively. The article introduces self-determination theory to the domain of work intensity and focuses on two hypotheses. The first is whether intensive work driven by explicit or implicit incentives is more positively associated with an employee's job satisfaction than intensive work driven by job demands. The second is whether intensive work driven by intrinsic motives is more positively associated with job satisfaction than that driven by explicit or implicit incentives. In both these cases, the article also examines whether equivalent effects exist on (reduced) quit intentions. Original data from a major Greek grocery chain provide corroborative evidence that is robust to a rich set of covariates, including increasingly demanding adjustments for job discretion. The findings contribute to a more complete understanding of why differences in well-being exist among employees performing intensive work, with implications for workers and employers.     

Analysis of the Efficacy of Public Spending on Research and Development in State Programs

This paper presents an approach to assessing the efficacy of public spending on research and development in state programs. At present, there is a need to develop a unified approach to the analysis and evaluation of the efficacy of such spending from the perspective of participants in state programs (federal executive bodies). The proposed approach does not allow evaluating the efficacy of public spending on the development of research areas assigned to the authors.     

Reference points for measuring social performance: Case study of a social business venture

We present a longitudinal qualitative case study to elaborate on how a social venture forms reference points for social performance. Although organizations increasingly use various social performance targets to direct their operations, the scholarly knowledge on social performance reference points remains limited. We make use of the prior accounting literature and draw on the idea of compromising accounts to discuss how provisional and performative metrics can have a significant role in how organizations develop new ways to evaluate their social performance. Given that the social performance reference point criteria are ambiguous and the corresponding referents malleable, performative accounts are helpful as they can intervene in the organizational life by making particular things visible, providing space for interpretations, and facilitating discussion, thus creating temporary settlements and enabling opportunities for productive compromises between different organizational groups and evaluative principles. The recursive feedback loops between reference point referents, criteria and accounting artefacts help the organization to make sense of its own social performance and interpret the associated performance feedback, and thereby provide ground for organizational decisions on further action. Moreover, we discuss how imperfect accounts can be useful for social businesses in their pursuit of developing their activities and achieving social impact.     

Can investors time their exposure to private equity?

Private equity performance, both for buyouts and venture capital, has been highly cyclical: periods of high fundraising have been followed by periods of low performance. Despite this seemingly predictable variation, we find modest gains, at best, to pursuing realistic, investable strategies that time capital commitments to private equity. This occurs, in part, because investors can only time their commitments to funds; they cannot time when commitments are called or when investments are exited. There is a high degree of time-series correlation in net cash flows even across commitment strategies that allocate capital in a very different manner over time.     

Entry limiting agreements: First‐mover advantage,authorized generics,and pay‐for‐delay deals

During patent litigation, pay‐for‐delay (P4D) deals involve a payment from a patent holder of a branded drug to a generic drug manufacturer to delay entry and withdraw the patent challenge. In return for staying out of the market, the generic firm receives a payment, and/or an authorized licensed entry at a later date, but before the patent expiration. We examine why such deals are stable when there are multiple potential entrants. We combine the first‐mover advantage for the first generic with the ability of the branded manufacturer to launch an authorized generic (AG) to show when P4D deals are an equilibrium outcome. We further show that limiting a branded firm's ability to launch an AG before entry by a successful challenger will deter such deals. However, removing exclusivity period for the first generic challenger will not.     

Bitcoin price and its marginal cost of production: support for a fundamental value

This study back-tests a marginal cost of production model proposed to value the digital currency Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining Bitcoin prices, challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices will tend to this bound and not collapse to zero.     

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.