Forecasting Performance Evaluation: Does a Complicated Model Outweigh a Simple One?

2003 ◽  
Author(s):  
Jongsoo Choi ◽  
Jeffrey S. Russell ◽  
Robert B. Miller
Keyword(s):  
Performance Evaluation ◽  
Forecasting Performance ◽  
Complicated Model

Investigation of factors influencing employee performance

2016 ◽  
Vol 24 (2) ◽  
pp. 340-368 ◽  
Author(s):  
Jiří Šindelář
Keyword(s):  
Performance Evaluation ◽  
Work Experience ◽  
Formal Education ◽  
Employee Performance ◽  
Organizational Factors ◽  
Personal Interview ◽  
Content Type ◽  
Organizational Environments ◽  
Forecasting Performance ◽  
Soft Factors

Purpose The purpose of this paper is to investigate the effect of selected organizational factors on the performance of employees charged with sales forecasting, and to compare this across the different organizational environments of Central-Eastern European (CEE) retail chains. Design/methodology/approach The research involves seven major pan-European retail chain companies, with a total number of 201 respondents. Data were collected via a questionnaire [computer-aided personal interview (CAPI) and human-aided personal interview (HAPI) method] with a five-point scale evaluation of both dependent (organizational factors) and independent (performance indicator) variables. Cluster analysis was then used to derive the characteristics of average organizational environments, and correlation analysis was used to investigate the direction and size of the performance effect. Findings The results confirmed that different organizational environments have differing effects on the performance of forecasters. It also showed that the “hard core” factors (performance evaluation and information systems) do not have a dominant effect on employee performance in any of the environments regardless of their quality, and are aggregately outclassed by “soft” factors (communication lines and management support). Finally, the research indicated that among the personal attributes related to individual forecasters, domain and forecasting work experience have significant, beneficial effects on forecasting performance, whereas formal education level was detected to have a negative effect and can be, at best, considered as non-contributor. Practical implications The research results along with available literature enable us to define four management theses (focus on system, less on people; soft factors are equal to hard ones; higher formal education does not contribute to forecasting performance; and do not overestimate the social and morale situation on the working place) as well as four stages of organizational development, creating a practitioner’s guide to necessary steps to improve an environment’s key factors, i.e. performance evaluation, information systems and forecasting work experience. Originality/value Although there are regular studies examining the effect of organizational factors on employee performance, very few have explored this relationship in a forecasting context, i.e. in the case of employees charged with sales forecasting. Furthermore, the paper brings evidence on this topic from the CEE area, which is not covered in most prominent forecasting management studies.

Performance Evaluation of Road Surface Temperature Forecasts

2020 ◽  
Author(s):  
Tae J. Kwon ◽  
Liping Fu
Keyword(s):  
Performance Evaluation ◽  
Surface Temperature ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Time Of Day ◽  
Forecast Horizon ◽  
Statistical Measures ◽  
Factors Influencing ◽  
Forecasting Performance ◽  
Bodies Of Water

This paper describes a study focusing on performance evaluation of RWIS surface temperature forecasts. To identify the factors influencing the accuracy of forecasts, five research hypotheses regarding climate patterns, geographical attributes, seasonal variations, time of day, and forecast-horizon are proposed. Observations and forecasts data were provided by four North American provinces and were processed and stratified by station, hour, and month to quantify their effects. The findings based on two statistical measures; namely, mean absolute error and percent of acceptable forecasts suggested that the maritime climate group had the highest correspondence while the mixed climate group had the lowest. The geographical attributes showed that the forecasting performance of coastal maritime regions was found to have a negative correlation to the distance from nearby large bodies of water. Forecasts for the daytime were found to be less accurate than for the nighttime and deteriorate quickly as the forecasting horizon increased.

Fine Particle Mass Monitoring with Low-Cost Sensors: Corrections and Long-Term Performance Evaluation

2019 ◽  
Author(s):  
Carl Malings ◽  
Rebecca Tanzer ◽  
Aliaksei Hauryliuk ◽  
Provat K. Saha ◽  
Allen L. Robinson ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Fine Particle ◽  
Low Cost ◽  
Particle Mass ◽  
Long Term Performance ◽  
Term Performance

Performance Evaluation Tests for Environmental Research (PETER): Code Substitution Test.

1981 ◽  
Author(s):  
Ross L. Pepper ◽  
Robert S. Kennedy ◽  
Alvah C. Bittner ◽  
Steven F. Wiker
Keyword(s):  
Performance Evaluation ◽  
Environmental Research
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