Household Determinants of the Adoption of Improved Cassava Varieties using DNA Fingerprinting to Identify Varieties in Farmer Fields: A Case Study in Colombia

We examine factors affecting the adoption of improved cassava varieties of 217 households in the Cauca Department in southwest Colombia. Using DNA fingerprinting through Single Nucleotide Polymorphisms (SNPs), we identified different cultivars in farmers fields. We also used this information to remove possible bias in the adoption model that could have resulted from a misclassification of improved varieties (IVs). As a result, we found that farmers substantially overestimate their use of IVs and there are important differences in the determinants of adoption between farmer self‐identification and DNA fingerprinting. This finding implies that the incorporation of DNA fingerprinting in IV adoption studies is important to ensure the accuracy of future agricultural economic research and the relevance of subsequent policy recommendations.     

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.     

Target‐Setting,Pay for Performance,and Quality Improvement: A Case Study of Ontario Hospitals’ Quality‐Improvement Plans

This study examines whether difficult targets and quality indicators in executives' pay‐for‐performance (P4P) plans affect performance. The impact of target‐setting and P4P plans on quality improvement in the public sector is unclear. The Ontario government initiated the Quality Improvement Plan (QIP), which requires hospitals to set targets for quality indicators annually and link executive pay to target achievement since 2011. Analyzing Health Quality Ontario's database and hospitals' 2012–2013 QIPs, this study shows greater quality improvement in hospitals with difficult targets than hospitals with easy targets or without assigned targets; however, the positive impact disappears for high‐performance hospitals relative to their peers. We find no significant effect of the use of quality indicators in executives' P4P plans on quality improvement. Copyright © 2018 ASAC. Published by John Wiley & Sons, Ltd.     

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.     

The equivalence of supporting software delegation and sales delegation

This paper considers a duopoly market with horizontally differentiated system goods to examine system owners' behaviors under supporting software delegation, in which owners of system firms use varieties of supporting software, coupled with profit, to evaluate their managers' performance. Supporting software delegation seems to induce managers to act more aggressively in price competition than sales delegation does; however, we prove that if two systems are compatible and the varieties of supporting software are determined by hardware owners' overall expenditure amount on software, then supporting software delegation is equivalent to sales delegation. Owners of system firms induce their managers to act less aggressively in hardware price competition by offering contracts with a negative weight on varieties of supporting software under supporting software delegation. We find that stronger network externalities do not reverse system owners' contracting behaviors under supporting software delegation. Finally, it is worth mentioning that hardware technologies are static in this paper. In other words, dynamic changes such as hardware evolution are not considered in our analysis.     

Biofuels technology development in Southern Africa

ABSTRACT

The benefits of biofuels depend on the feedstock, conversion pathway and local context. This paper assesses biofuels technology readiness and developments to provide foresight to biofuels development in Southern Africa. Efficient conversion pathways, coupled with biomass from waste or high-yielding energy crops, will reduce both the costs of biofuels production, and the environmental impacts. Compared to petroleum fuels, the current commercial biofuels (ethanol, biogas and biodiesel) typically offer carbon emission reductions of 30–50% but are marginally more expensive. The extent of biofuels market penetration will therefore be influenced by mandates (blending targets) and subsidies (green premium). Advanced biofuels promise greater efficiencies and carbon emission reductions at reduced cost but will require further research and development to reach commercialisation. If developed appropriately, biofuels can reduce carbon emissions and improve energy security, while enabling sustainable agriculture and improved natural resources management.     

Effects of scale economies and production seasonality on optimal hub locations: the case of regional fresh produce aggregation

Interest in supporting local and regional food systems is rising and food hubs have attracted considerable attention among Federal, State, and local policymakers. This study explores the problem of endogenous hub location in fresh produce value chains in the Northeastern United States. To overcome limitations in the literature, we incorporate the effects of economies of scale and production seasonality into our models. Three experimental models are designed to examine the effects of alternatively applying yearly, quarterly, and monthly data on model solutions. We explicitly assess how interactions of scale economies and seasonality influence the structure and spatial attributes of an optimal regional produce aggregation hub system. The three models generate marketed different solutions and in many respects they lead to different conclusions about developing local/regional supply chains. The monthly model allows for production seasonality and actual hub operation cycle frequency and thus leads to more efficient hub solution with rich policy implications.     

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.     

The practice of collective bargaining in the private sector in Quebec (Canada): the changing ‘rules of the game’

Drawing on 45 semi‐structured interviews with union negotiators active in the Quebec private sector, this article shows that local bargaining practices, despite their plurality, have tended to change following major trends. It also reveals, more fundamentally, a redefinition of the ‘rules of the game’. The transformation and stability of these social rules, which are much more focused on the needs of employers, have tended to weaken collective bargaining as a tool for industrial democracy.