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The paper aims to estimates the limitations of a forced distribution performance appraisal system in identifying the highest performing individuals within an organization. Traditionally, manpower modeling allows organizations to develop plans that meet future human resource requirements by modeling the flow of personnel within an organization. The aim is to quantify the limitations of a performance appraisal system in identifying the best-qualified individuals to fill future requirements.

This paper describes an exploratory study using discrete event simulation based on the assignment, evaluation and promotion history of over 2,500 officers in the US Army. The obtained data provide a basis for estimating simulation inputs that include system structure, system dynamics, human behavior and policy constraints. The simulation approach facilitates modeling officers who receive evaluations as they move throughout the system over time.

The paper provides insights into the effect of system structure and system dynamics on the evaluation outcome of employees. It suggests that decreasing the number of a rater’s subordinates has a significant effect on the accuracy of performance appraisals. However, increasing the amount of time individuals spend on each assignment has little effect on system accuracy.

This research allows an organization’s leadership to evaluate the possible consequences associated with evaluation policy prior to policy implementation.

This work advances a framework in assessing the effect of system dynamics and structure, and the extent to which they limit or enhance the accuracy of an organization’s forced distribution performance appraisal system.

In this paper, we consider a two color multi-drawing urn model. At each discrete time step, we draw uniformly at random a sample of m balls (m≥1) and note their color, they will be returned to the urn together with a random number of balls depending on the sample’s composition. The replacement rule is a 2 × 2 matrix depending on bounded discrete positive random variables. Using a stochastic approximation algorithm and martingales methods, we investigate the asymptotic behavior of the urn after many draws.

The purpose of the paper is to present an empirical study that examines the impact of digitalization on informative, strategic and operational marketing activities in manufacturing companies from the entrepreneurial perspective.

A research project was carried out in 205 Italian manufacturing companies by using the questionnaire method. An exploratory research study was conducted with hierarchical cluster analysis.

The analysis shows the existence of seven clusters of manufacturing companies that differ by the impact of digitalization on marketing activities from the entrepreneurial perspective. Two clusters have a high positive impact of digitalization, primarily on informative and strategic marketing activities. Two clusters are characterized by a low positive impact of digitalization and three clusters perform an intermediate level of digitalization. Furthermore, these groups of clusters differ in terms of the influence of digitalization on customer value.

The small size of the sample and the geographic origin of the companies imply limited generalizability; further research on the topic is thus recommended.

The study suggests that companies should digitalize many key marketing activities to increase marketing effectiveness and customer value. To achieve high levels of digitalization and thus increase their competitiveness, manufacturing companies should consider the importance of relevant technologies and skills.

By focussing on the impact of digitalization on informative, strategic and operational marketing, which has not yet been empirically investigated, the present study reveals many new elements concerning the marketing process in the digital era from the entrepreneur's point of view.