An integrative model of e-learning use: Leveraging theory to understand and increase usage

Employees are increasingly using technology to access content for learning, and theory development has been outpaced by practice. Drawing on a well validated theory of behavior change (the Transtheoretical Model of Change), as well as theories on technology acceptance and employee development, this paper offers an integrative model of factors that influence employee use of e-learning as well as practical recommendations for how use might be increased. Recommendations for future research on e-learning are also offered.     

Digging deeper or piling it higher? Implicit measurement in organizational behavior and human resource management

Organizational researchers can dig deeper into peoples' thoughts, attitudes, and self-concepts to understand how automatic processes may impact judgment and social behavior in organizations. Measures of these automatic processes, including the Implicit Association Test (e.g., IAT; Greenwald, McGhee, & Schwartz, 1998), Semantic Priming (e.g., SP; Wittenbrink, Judd, & Park, 1997), Affect Misattribution Procedure (e.g., AMP; Payne, Cheng, Govorun, & Stewart, 2005), Word Completion Tasks (e.g., WCT; Johnson & Saboe, 2011), among many others, deserve greater attention as alternatives or supplements to traditional self-report measures of variables important in organizations (e.g., job satisfaction, personality and trait measurement, diversity attitudes). In this paper, we first provide a primer on implicit social cognition and its relationship to automatic and controlled cognitive processes, discussing major types of implicit measures, how these might operate, criticisms of this approach, and how these implicit constructs may give rise to behavior in organizations. Second, we discuss models of automatic processes and explore their validity and how these may predict behavior. Third, we offer advice for selecting, constructing, and improving implicit measurements when used in organizational research to enhance human resources and organizational functioning.     

Leader-member exchange,employee performance,and work outcomes: an empirical study in the Chinese context

In this study, we employed the multidimensional view of LMX (LMX-MDM) to develop a model that captured different antecedents and outcomes of task and contextual performance. We tested this model with a sample drawn from subjects from the People's Republic of China. The results indicated that the affect dimension of LMX-MDM was positively associated with task performance and contextual performance. The contribution dimension of LMX-MDM was positively related to the job dedication dimension of contextual performance. Supervisory ratings of the task performance and the interpersonal facilitation dimension of contextual performance predicted the promotability of subordinates, and task performance influenced subordinates' intention to quit. Limitations and future research are discussed.     

The economic costs of biological predation

This paper developes a bioeconomic model to analyse the economic losses from the reduced harvesting of prey species resulting from an increase in the stock of a natural predator. Examples of large mammals creating economic damage are whales and African elephants. The economic losses depend critically on the actual management of the prey stock, although the three measures we develop are equal when the stock is managed so as to maximize the sustained economic rent from the prey species. Predation losses are illustrated by the case of the Northeastern Atlantic Minke whale, where the estimate of the average predation cost per whale in 1991–1992 is between $US 1780 and $US 2370, using Norwegian cost and earnings data. A ten percent stock increase is estimated to cause a loss of almost $US 19 million to the fishers of the prey species. If half of this cost were assigned to Norway it would be equivalent to 2.8 and 6.7 percent of the gross profits of the Norwegian cod and herring fisheries, respectively.     

EXPLORING THE USEFULNESS OF A NONRANDOM HOLDOUT SAMPLE FOR MODEL VALIDATION: WELFARE EFFECTS ON FEMALE BEHAVIOR*

A particularly challenging use of decision‐theoretic models in economics is to forecast the impact of large changes in the environment. The problem we explore in this article is how to gain confidence in a model's ability to predict the impact of such large changes. We show that an approach to validation and model selection that includes the choice of a “nonrandom holdout sample,” a sample that differs significantly from the estimation sample along the policy dimension that the model is meant to forecast, can be fruitful.     

A Brief Survey of Modern Optimization for Statisticians

Modern computational statistics is turning more and more to high‐dimensional optimization to handle the deluge of big data. Once a model is formulated, its parameters can be estimated by optimization. Because model parsimony is important, models routinely include non‐differentiable penalty terms such as the lasso. This sober reality complicates minimization and maximization. Our broad survey stresses a few important principles in algorithm design. Rather than view these principles in isolation, it is more productive to mix and match them. A few well‐chosen examples illustrate this point. Algorithm derivation is also emphasized, and theory is downplayed, particularly the abstractions of the convex calculus. Thus, our survey should be useful and accessible to a broad audience.     

Scoring rules and survey density forecasts

This article provides a practical evaluation of some leading density forecast scoring rules in the context of forecast surveys. We analyse the density forecasts of UK inflation obtained from the Bank of England’s Survey of External Forecasters, considering both the survey average forecasts published in the Bank’s quarterly Inflation Report, and the individual survey responses recently made available to researchers by the Bank. The density forecasts are collected in histogram format, and the ranked probability score (RPS) is shown to have clear advantages over other scoring rules. Missing observations are a feature of forecast surveys, and we introduce an adjustment to the RPS, based on the Yates decomposition, to improve its comparative measurement of forecaster performance in the face of differential non-response. The new measure, denoted RPS*, is recommended to analysts of forecast surveys.