Mark Eakin

A longitudinal study of newcomer role adjustment in US organizations

Abstract This paper examines the socialization and adjustment experiences of newcomers to organizations from a role stress perspective. A longitudinal field study is reported which tested a three-stage socialization model using physiological and psychological distress symptoms, general satisfaction, and intention to leave the organization as indicators of newcomer adjustment. Data were collected from newcomers (the focal role) (N = 91) at three times during me study and also from their supervisors (the role senders) (N = 41). While the data confirmed the general utility of the model, it also suggested some revisions by way of new and altered pathways between some of the variables.

Beyond organizational entry and newcomer stress: Building a self-reliant workforce

This article extends the organizational entry and newcomer stress literature by focusing on the importance of building a self-reliant workforce. It posits that newcomer socialization now and in the future requires building a workforce of self-reliant managers and employees who can function effectively in a dynamic, global workplace. This article suggests that socializing workers to be self-reliant helps employees and organizations to thrive in the uncertain economic environment that is the determining factor in the success of todays organizations. It further contends that by reducing ambiguity and increasing employability, employees are likely to suffer less distress and organizations are less likely to pay the price for distressed employees.

Incorporating Unusual Fonts and Planned Mistakes in Study Materials to Increase Business Student Focus and Retention

New studies indicate that incorporating planned difficulties such as unusual font styles in study material forces students to focus and concentrate more. In our experiment 155 students under different instructors learn a core topic in operations management in an active learning environment. One group of students in each class used a standard version of the study materials while two other groups used a modified version incorporating an unusual difficult-to-read font or planned mistakes. Students with the unusual font materials scored highest on a post-workshop comprehension test. Students who had workshop materials with pedagogical mistakes scored second followed by students in the control group. Further, it was observed that students in the control group deceived themselves with respect to sense of material mastery.

A Better Estimate of the Number of Valid Signatures on a Petition

The 2006 Texas gubernatorial race with independent candidates Kinky Friedman and Carole Keeton Strayhorn, the 2004 attempts of Ralph Nader to gain ballot access as an independent candidate for President of the United States, and the recall of California Governor Gray Davis in 2003 are among many examples that illustrate the importance of validating signatures on a petition. Signatures may be invalid for several reasons, including not a registered voter and replication. The statistical problem is interesting because replicated signatures must be estimated differently than other invalid signatures. This article presents a new nonlinear estimator that is unbiased with smaller standard error in cases considered and suggests an innovation to current practice: first estimate the number of replicated signatures, then the number of valid signatures.

The sampling distribution of the W estimator of the number of valid signatures on a petition

Abstract A new non linear estimator, W, for the number of valid, unique signatures on a petition has been shown better, for the cases enumerated and with certain restrictions, than a popular Goodman-type statistic, G. This article extends those results with relaxed conditions by developing the exact probability mass function and mean of W and a close approximation of the variance (Var(W)). If the proportion of valid signatures among unique and duplicated signatures is the same, then Var(W) is approximately a function of the means and variances of the two sample statistics. Using the delta method, we estimate Var(W), with the resulting approximation shown to be good, even when the condition of equal proportions does not hold. We compare W to G and establish which estimator is preferred for different intervals of the design parameters. Data from a Washington State petition illustrate the findings.

Modified chance constrained linear programming under uncertainty

This paper considers the sensitivity of chance constrained linear programming solutions where the coefficients of the left-hand side of a constraint function are estimated from a sample using multiple linear regression. The modified nonlinear constraint provides considerable assurance that the true, but unknown, stochastic linear constraint will be satisfied at a given level of probability for the conditions of the simulation herein. Ordinary least squares and least absolute value regression criteria are considered along with normal, uniform and double exponential distributions of error.