An Analytical Employee Performance Evaluation Approach in Office Automation and Information Systems

With the extension of information technology, human resource management has experienced fundamental changes. One of the most important issues in human resource management is performance evaluation. Unlike number of studies in employee performance evaluation, there is a lack for systematic and quantitative approaches. Issues such as incomplete information, subjective and qualitative metrics, and also the difficulty of evaluating the performance are the main problems of this field. Hence, the current study exploits the capabilities of information systems and presents an approach for quantitative and automatic evaluation of employee performance in office automation systems. The results reveal the automatic employee performance evaluation system is a discrete dimension for employee performance evaluation systems.

Analytics Overuse in Advertising and Promotion Budget Forecasting

Several studies have recently raised a common concern in the field of management, which is the overspending in marketing activities. In this paper, we propose and empirically test that overspending in marketing investments is an unfortunate outcome of information overload, in a sense that managers who confront too many risk informants in their decision environment tend to overinvest in marketing activities due to the overemphasis on the environmental risk. In a longitudinal experiment, where we manipulated the amount of information through marketing analytics, we demonstrate that firms employing simple marketing analytics are less prone to increase their marketing expenditures due to the fear of losing customers, and have a lower expectancy that their competitors will increase their brand-level advertising and promotional expenditures, compared to firms using a combination of simple and complex marketing analytics. Moreover, we demonstrate that firms employing simple marketing analytics keep their overall marketing spending at a lower level, and spend less in brand-level marketing, especially in promotional activities, compared to when using a combination of simple and complex marketing analytics.

Predictive Modeling as guide for Health Informatics Deployment

The present research work shows the main steps conducted towards the exploitation of the LUMIR project, aiming at realizing an EHR framework in the Italian Region of Basilicata (also known as Lucania). It relates to a structure of network–enabled services capable of integrating the ICT solutions used by the operators of the Healthcare System of Basilicata Region. The adoption process of the LuMiR system was meant to address the issues connected to the design features as well as to the EHR diffusion and the acceptance aspects. The mathematical modeling approach introduced aimed at making possible to get to a measure “ex–ante” of both adequacy and significance of the adoption process itself. The final intent is to work out a scalable and exportable model of advanced management of clinical information, towards a stronger cooperation among the provider organizations and a better governance of care processes, as crucial element within the more general path of modernization of the healthcare sector.

A Hybrid AHP-ELECTRE I Multicriteria Model for Performance Assessment and Team Selection

Human resources management is essential to any health care system. This paper proposes an assessment model to help the decision maker in the selection of an optimal team. In the proposed model, AHP method is applied to identify the weights of each criterion in the decision model. ELECTRE I method is used to obtain the best team that satisfies most of the decision maker preferences. We test the effectiveness of the model on the real data collected from the ‘Habib Bourguiba' Hospital in Tunisia.

Student Retention Performance Using Absorbing Markov Chains

Performance models are well established in the literature. More specifically, student performance has been of growing concern at all levels. To confront the challenges, researchers have collected data, monitored performance criterion, developed quantitative models, and analyzed patterns to formulate theories and adaptive measures. At the university level, many students' performance deficiencies are keenly noticed and actualized for a variety of reasons. Some reasons may include transition from a home-reporting educational environment to an autonomous setting; lack of a friendly support system; or a host of behavioral circumstances which exacerbate latent academic deficits. One such technique for reviewing student performance can be employed and analyzed using absorbing Markov chains. The use of Markov Chains can provide quantitative information such the characterization potential delays (latency points) within and throughout the system, prediction of probabilistic metrics which define transitions between each stage of a defined state, and adaptability options for enrollment outcomes for use by school administrators. Furthermore, Markov chains can be employed to determine the impact on system resources such as limitations in faculty schedules, classroom assignments, and technology availability. Managers, administrators and advisors may find this information useful when notified of such limitations. This paper is of value to a broad audience such as researchers, managers, and administrators since it augments standard approaches of the Markov model. The blend of stochastic mathematics, applications of stochastic methods and retention theory, as well as the inclusion of adaptive sensitivity analysis are effective performance measures. Therefore, applications in Markov chains and subsequent forecasting models are of contemporary values in educational performance. Each of these concepts and methods contribute to a broader consideration of Markov properties in a branch of mathematics known as Markov Decision Processes (MDP). These types of processes allow researchers the ability to adjust parameters based on rewards, sets of actions, and discount factors. The cases outlined in this paper may be helpful when considering reductions in recidivism rates, improving policies to diminish recidivism, and increasing enrollment options using Markov analysis.

Information and Communication Technology Impact on Supply Chain Integration, Flexibility, and Performance

In this chapter, four latent variables will be analyzed to measure the impact of Information and Communications Technology (ICT) on the integration, flexibility and performance of Supply Chain (SC). The aim of the exposition is to provide greater understanding for those responsible of the supply chain, and focus efforts on clear objectives. These clear objectives should help those responsible for the supply chain achieve a better performance within organizations. The information analyzed was obtained from a questionnaire provided to 284 managers in companies located in Ciudad Juarez, Mexico. The results were used to generate a structural equation model in order to learn the relationships between variables. We have postulated six hypotheses regarding the direct, indirect and total effects. The results indicate that there is no direct relationship between ICT integration and SC performance, but an indirect relationship through mediating variables as SC Integration and Flexibility exists.

A Conceptual and Pragmatic Review of Regression Analysis for Predictive Analytics

Regression analysis and modeling are powerful predictive analytical tools for knowledge discovery through examining and capturing the complex hidden relationships and patterns among the quantitative variables. Regression analysis is widely used to: (a) collect massive amounts of organizational performance data such as Web server logs and sales transactions. Such data is referred to as “Big Data”; and (b) improve transformation of massive data into intelligent information (knowledge) by discovering trends and patterns in unknown hidden relationships. The intelligent information can then be used to make informed data-based predictions of future organizational outcomes such as organizational productivity and performance using predictive analytics such as regression analysis methods. The main purpose of this chapter is to present a conceptual and practical overview of simple- and multiple- linear regression analyses.

Effective Tools for Improving Employee Feedback during Organizational Change

Feedback is the fastest and most effective way for organizations to make improvements or get things back on track. Prompt and constructive feedback is strongly linked to employee satisfaction and productivity, and can increase both. During times of change when employees want to be heard and feel involved, it is even more important that the optimal internal communication tools for managing employee feedback are selected. This article tackles these questions and provides fresh empirical data on the selection of internal communication tools in general, with focus then devoted to managing feedback during change from the perspective of a professional communicator. The data evaluated and analyzed was gathered on the basis of research carried out in 2014 among 105 professional communicators of large and medium-sized companies, and was then compared with the results of similar research conducted in 2012.

Transformation of CRM and Supply Chain Management Techniques in a New Venture

The implementations of successful Customer Relationship Management (CRM) and Supply Chain Management (SCM) systems and their associated techniques in order to optimize the analytics available in any organization are daunting task, especially in a new business venture. Upper management must to be committed to focusing these embedded systems in order to enhance supplier integration and customer satisfaction. This chapter focuses on the implementation of CRM systems and analytics as well as SCM considerations in the new startup of the Hard Rock Rocksino at Northfield Park (HRRNP) and the transformation/refinement of their systems over their few years of business. A combination of literature research, interviews of upper management, and personal observations, HRRNP has illustrate their ability to deal with these challenges in a continuous improvement and lean management approach.

New Product Development and Manufacturability Techniques and Analytics

The following case study evaluates the New Product Development (NPD) techniques utilized by Forest City Technologies, Incorporated (FCT). Through insight gathered via interviews conducted with the company's product development and materials purchasing management teams, and supported by literature, this study attempts to show how Forest City Technologies, Inc. integrates specific components into its product development process to: 1. Meet its NPD goals, and 2. Achieve better supplier and customer relationships. This study focuses on the components of: NPD models employed by FCT, early customer and supplier involvement, NPD-innovation integration techniques, demand change factors during the NPD process, and risk-mitigation strategies implemented by FCT during the NPD process. The study is segmented into three main sections: Introduction to NPD and FCT, the components of FCTs new product development process, and NPD implications on FCTs supplier and customer relationships.