How does firm innovativeness enable supply chain resilience?:The moderating role of supply uncertainty and interdependence

Despite its potential benefits in a wide range of circumstances, firm innovativeness received scant attention in relation to managing the various risks and uncertainties in the global business environment. Likewise, there is still a limited understanding of firms’ supply chain resilience (SCR) and its related antecedents in the strategic management literature. This research focuses on exploring the relationship between firm innovativeness and SCR in an attempt to facilitate bridging the gap between two important research streams and shed some light on the contingent value of firm innovativeness against disruptions and adversities. The moderating role of supply uncertainty and interdependence in the focal relationship was also hypothesised and tested. Findings suggest that firm innovativeness is positively associated with firm SCR, and supply uncertainty negatively moderates this relationship but interdependence does not. We argue that this could be due to the dual nature of interdependence in supply networks

Standard survey methods for estimating colony losses and explanatory risk factors in Apis mellifera

This chapter addresses survey methodology and questionnaire design for the collection of data pertaining to estimation of honey bee colony loss rates and identification of risk factors for colony loss. Sources of error in surveys are described. Advantages and disadvantages of different random and non-random sampling strategies and different modes of data collection are presented to enable the researcher to make an informed choice. We discuss survey and questionnaire methodology in some detail, for the purpose of raising awareness of issues to be considered during the survey design stage in order to minimise error and bias in the results. Aspects of survey design are illustrated using surveys in Scotland. Part of a standardized questionnaire is given as a further example, developed by the COLOSS working group for Monitoring and Diagnosis. Approaches to data analysis are described, focussing on estimation of loss rates. Dutch monitoring data from 2012 were used for an example of a statistical analysis with the public domain R software. We demonstrate the estimation of the overall proportion of losses and corresponding confidence interval using a quasi-binomial model to account for extra-binomial variation. We also illustrate generalized linear model fitting when incorporating a single risk factor, and derivation of relevant confidence intervals

Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: a tutorial.

BACKGROUND: Data on survival endpoints are usually summarised using either hazard ratio, cumulative number of events, or median survival statistics. Network meta-analysis, an extension of traditional pairwise meta-analysis, is typically based on a single statistic. In this case, studies which do not report the chosen statistic are excluded from the analysis which may introduce bias. METHODS: In this paper we present a tutorial illustrating how network meta-analyses of survival endpoints can combine count and hazard ratio statistics in a single analysis on the hazard ratio scale. We also describe methods for accounting for the correlations in relative treatment effects (such as hazard ratios) that arise in trials with more than two arms. Combination of count and hazard ratio data in a single analysis is achieved by estimating the cumulative hazard for each trial arm reporting count data. Correlation in relative treatment effects in multi-arm trials is preserved by converting the relative treatment effect estimates (the hazard ratios) to arm-specific outcomes (hazards). RESULTS: A worked example of an analysis of mortality data in chronic obstructive pulmonary disease (COPD) is used to illustrate the methods. The data set and WinBUGS code for fixed and random effects models are provided. CONCLUSIONS: By incorporating all data presentations in a single analysis, we avoid the potential selection bias associated with conducting an analysis for a single statistic and the potential difficulties of interpretation, misleading results and loss of available treatment comparisons associated with conducting separate analyses for different summary statistics

THE ROLE OF INTERDEPENDENCE IN THE MICRO-FOUNDATIONS OF ORGANIZATION DESIGN: TASK, GOAL, AND KNOWLEDGE INTERDEPENDENCE

Interdependence is a core concept in organization design, yet one that has remained consistently understudied. Current notions of interdependence remain rooted in seminal works, produced at a time when managers’ near-perfect understanding of the task at hand drove the organization design process. In this context, task interdependence was rightly assumed to be exogenously determined by characteristics of the work and the technology. We no longer live in that world, yet our view of interdependence has remained exceedingly task-centric and our treatment of interdependence overly deterministic. As organizations face increasingly unpredictable workstreams and workers co-design the organization alongside managers, our field requires a more comprehensive toolbox that incorporates aspects of agent-based interdependence. In this paper, we synthesize research in organization design, organizational behavior, and other related literatures to examine three types of interdependence that characterize organizations’ workflows: task, goal, and knowledge interdependence. We offer clear definitions for each construct, analyze how each arises endogenously in the design process, explore their interrelations, and pose questions to guide future research

Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks

peer reviewedaudience: researcher, professionalVarious approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods, i.e.multiple linear regression and artificial neural networks, that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling. Artificial neural networks (ANNs) are combined with a generalized likelihood uncertainty estimation (GLUE) approach to predict Ks from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from literature demonstrates the importance of site specific calibration. The dataset used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size -Ks pairs. Finally, an application with the optimized models is presented for a borehole lacking Ks data

The Online Shadow of Offline Signals: Which Sellers Get Contacted in Online B2B Marketplaces?

This article extends the understanding of what impels buyers to contact particular sellers in online business-to-business (B2B) marketplaces, which are typically characterized by sparse social structures and concomitant limitations in observing social cues. Integrating an institutional perspective with signaling theory, our core argument is that offline seller characteristics that are visible online—in particular, geographic location and legal status—convey credible signals of seller behavior because they provide buyers with information on sellers’ local institutional quality and the institutionally-induced obligations and controls acting on sellers. Using unique data from a large Italian online B2B marketplace between the fourth quarter of 1999 and July 2001, we find that both sellers’ local institutional quality and their legal statuses affect a buyer’s likelihood of contacting a seller. Moreover, consistent with the idea that a buyer’s own local institutional quality generates a relevant reference point against which sellers are evaluated, we find that a buyer is progressively more likely to contact sellers the higher their local institutional quality relative to the buyer. Jointly, our findings imply that in online B2B marketplaces, signals conveyed by sellers’ geographic locations and legal statuses may constitute substantive sources of competitive heterogeneity and market segmentation

Effectiveness of a primary care-based intervention to reduce sitting time in overweight and obese patients (SEDESTACTIV): a randomized controlled trial; rationale and study design

Background: There is growing evidence suggesting that prolonged sitting has negative effects on people's weight, chronic diseases and mortality. Interventions to reduce sedentary time can be an effective strategy to increase daily energy expenditure. The purpose of this study is to evaluate the effectiveness of a six-month primary care intervention to reduce daily of sitting time in overweight and mild obese sedentary patients. Method/Design: The study is a randomized controlled trial (RCT). Professionals from thirteen primary health care centers (PHC) will randomly invite to participate mild obese or overweight patients of both gender, aged between 25 and 65 years old, who spend 6 hours at least daily sitting. A total of 232 subjects will be randomly allocated to an intervention (IG) and control group (CG) (116 individuals each group). In addition, 50 subjects with fibromyalgia will be included. Primary outcome is: (1) sitting time using the activPAL device and the Marshall questionnaire. The following parameters will be also assessed: (2) sitting time in work place (Occupational Sitting and Physical Activity Questionnaire), (3) health-related quality of life (EQ-5D), (4) evolution of stage of change (Prochaska and DiClemente's Stages of Change Model), (5) physical inactivity (catalan version of Brief Physical Activity Assessment Tool), (6) number of steps walked (pedometer and activPAL), (7) control based on analysis (triglycerides, total cholesterol, HDL, LDL, glycemia and, glycated haemoglobin in diabetic patients) and (8) blood pressure and anthropometric variables. All parameters will be assessed pre and post intervention and there will be a follow up three, six and twelve months after the intervention. A descriptive analysis of all variables and a multivariate analysis to assess differences among groups will be undertaken. Multivariate analysis will be carried out to assess time changes of dependent variables. All the analysis will be done under the intention to treat principle. Discussion: If the SEDESTACTIV intervention shows its effectiveness in reducing sitting time, health professionals would have a low-cost intervention tool for sedentary overweight and obese patients management

Variability Modifies Life Satisfaction\u27s Association With Mortality Risk In Older Adults

Greater life satisfaction is associated with greater longevity, but its variability across time has not been examined relative to longevity. We investigated whether mean life satisfaction across time, variability in life satisfaction across time, and their interaction were associated with mortality over 9 years of follow-up. Participants were 4,458 Australians initially at least 50 years old. During the follow-up, 546 people died. After we adjusted for age, greater mean life satisfaction was associated with a reduction in mortality risk, and greater variability in life satisfaction was associated with an increase in mortality risk. These findings were qualified by a significant interaction such that individuals with low mean satisfaction and high variability in satisfaction had the greatest risk of mortality over the follow-up period. In combination with mean life satisfaction, variability in life satisfaction is relevant for mortality risk among older adults. Considering intraindividual variability provides additional insight into associations between psychological characteristics and health

Stock market investors' use of stop losses and the disposition effect

The disposition effect is an investment bias where investors hold stocks at a loss longer than stocks at a gain. This bias is associated with poorer investment performance and exhibited to a greater extent by investors with less experience and less sophistication. A method of managing susceptibility to the bias is through use of stop losses. Using the trading records of UK stock market individual investors from 2006 to 2009, this paper shows that stop losses used as part of investment decisions are an effective tool for inoculating against the disposition effect. We also show that investors who use stop losses have less experience and that, when not using stop losses, these investors are more reluctant to realise losses than other investors