How deficiencies in motivation to manage contribute to the United States' competitiveness problem (and what can be done about it)

Data are presented to support the argument that motivation to manage is a major cause of managerial effectiveness, that it declined sharply in the college population during the activism of the 1960s and early 1970s, and that it is now severely lacking in the United States relative to many other countries. Thus America's competitiveness problems appear to be largely motivational in nature. Possible solutions are discussed, including recruiting and selecting for managerial motivation, increasing motivation to manage using training and development techniques, and changing organizational designs (telescoping the scalar chain, moving managerial tasks into non-managerial positions, resorting to professional forms, and expanding venture structures). © 1995 by John Wiley & Sons, Inc.     

Effects of service orientation on job embeddedness in hotel industry

This research aims to develop and analyze a model that depicts work engagement (WE) as a mediator of the relationship between job embeddedness and service orientation. Specifically, the model examines external environmental factors (EEFs) as moderator of the effects of service orientation and job embeddedness in the hospitality industry. All data used for this study were gathered in Iran from hotels frontline employees with a two-weeks’ time lag. These relationships mentioned above were analyzed using AMOS 22.0. It was discovered in the results that WE was indeed a partial mediator and that EEFs indeed moderated the effects of service orientation on job embeddedness with adequate empirical support. The implications of the findings for the managers, the study limitations, and future research recommendations were also discussed.     

Feedforward versus recurrent neural networks for forecasting monthly japanese yen exchange rates

Neural networks are a relatively new computer artificial intelligence method which attempt to mimic the brain's problem solving process and can be used for predicting nonlinear economic time series. Neural networks are used to look for patterns in data, learn these patterns, and then classify new patterns and make forecasts. Feedforward neural networks pass the data forward from input to output, while recurrent networks have a feedback loop where data can be fed back into the input at some point before it is fed forward again for further processing and final output. Some have argued that since time series data may have autocorrelation or time dependence, the recurrent neural network models which take advantage of time dependence may be useful. Feedforward and recurrent neural networks are used for comparison in forecasting the Japanese yen/US dollar exchange rate. A traditional ARIMA model is used as a benchmark for comparison with the neural network models.Results for out of sample show that the feedforward model is relatively accurate in forecasting both price levels and price direction, despite being quite simple and easy to use. However, the recurrent network forecast performance was lower than that of the feedforward model. This may be because feed forward models must pass the data from back to forward as well as forward to back, and can sometimes become confused or unstable. Both the feedforward and recurrent models performed better than the ARIMA benchmark model.The author wish to thank the reviewers Drs. Kraft and Radford for their helpful comments.     

Tourism climate insurance: implications and prospects

This paper investigates the intentions of tourists to purchase climate insurance as part of their holiday packages. A research model, based on Bagozzi’s reformulation of attitude theory designed to examine the relationships between climate expectation-disconfirmation and the behavioral intentions of tourists in purchasing tourism climate insurance, with destination loyalty as an outcome. The model hypothesizes and tests whether expectation-disconfirmation can be resolved by insuring the product-climate. A structural equation modeling is utilized. The results reveal that the expectation-disconfirmation of tourists in relation to climate is positively associated with their intentions to purchase insurance and negatively affects loyalty.     

Neural Networks for Forecasting: An Introduction

Neural networks are nonlinear mapping structures based on the study of the human brain. They have been shown to be universal and highly flexible junction approximators to any data-generating process. Therefore, they are powerful models for forecasting purposes, especially when the underlying data-generating processes are unknown. However, the appropriate design of the network's architecture and learning rules are crucial for obtaining satisfactory results. This study discusses the scope and limitations of neural networks for forecasting problems and provides an example by designing a neural network for forecasting. It is argued that statistical theory can offer some suggestions for designing an optimal network architecture. An example comparing a neural network and ARIM model for forecasting weekly corn prices 1974 through 1993 is provided. Results show the neural network model to be more accurate than the ARIMA.     

Managerial Role Motivation and Role-related Ethical Orientation in Hong Kong

Journal of Business Ethics - Is there a relationship between the psychological construct of hierarchic managerial role motivation and the moral construct of role-related ethical orientation? In...     

Stressors,Coping Mechanisms,Perceived Supervisory Support,and Well-Being Among Nurses During Accreditation Surveys: a Study from UAE

Employee Responsibilities and Rights Journal - Organizational psychology applies psychological theories to improve the physical and mental well-being of employees, increase productivity, and...     

The effects of extremes and temporal scale on multifractal properties of river flow time series

For accurate forecasting of extreme events in rivers, streamflow time series with sub‐daily temporal resolution (1–6 hour) are preferable, but discharge time series for long rivers are usually available at daily or monthly resolution. In this study, the scaling properties of hourly and daily streamflow time series were measured. As an innovation, the effects of extreme values on the multifractal behavior of these series were evaluated. Interestingly, both hourly and daily discharge records led to nearly identical scaling trends and identical crossover times. Daily and hourly discharge time series appeared to be non‐stationary when the timescale ranged from 75 to 366 days. Otherwise, the signals may be considered stationary time series. In addition, the results indicated that the extreme values strongly contribute to the multifractality of the series. The width of singularity spectra decreased considerably when the extreme events were removed from both hourly and daily discharge records.     

Regionalization of Tank Model Using Landscape Metrics of Catchments

To address the challenges inherent in accessing spatiotemporal hydrological data, water resources professionals have developed various regionalization tools. The present study examines the possibility that changes in landscape metrics including mean shape index, mean perimeter-area ratio, mean patch size and patch density of land use/ land cover could result in variations in the optimized parameters of the conceptual rainfall-runoff Tank model. Data from 30 catchments that are geographically distributed in Germany was used to develop the procedure. Regression analysis-based modeling indicated that four out of twelve model parameters (r²?≥?0.40) can be explained by changes in catchment geometrics along with a set of landscape metrics of land use/land cover. They include: coefficient of infiltration flow (r²?=?0.48, p?<?0.03), intermediate flow (r²?=?0.77, p?<?0.02), water storage level for sub-surface flow (r²?=?0.57, p?<?0.05) and water storage level for intermediate flow (r²?=?0.85, p?<?0.01). Despite developing fairly reliable regression models, uncertainty analysis also revealed that uncertainty induced unreliability of the regionalized models is of significant importance.     

Similarity Metrics-Based Uncertainty Analysis of River Water Quality Models

Uncertainty analysis (UA) is essential to reinforce the decisions made by water resource engineers and managers. In this study, the stepwise multiple linear regression procedure assessed the relationship between water quality parameters and physical characteristics of 48 catchments in the southwestern basin of the Caspian Sea, Iran. The results of the modeling showed that the coefficient of determination ranged between 0.47 and 0.68 and indicated a positive relationship between the area (%) of agricultural lands and the sodium adsorption ratio (SAR), potassium (K) and total dissolved solids (TDS). A negative relationship was also found between bicarbonate (HCO₃^?) and the area (%) of the intermediate-density forest. In contrast to previous studies focusing on analyzing the uncertainty of the model parameters, we addressed the uncertainty of the model variables. The results of the GLUE-based uncertainty analysis (UA) performed on the model’s variables indicated that the measures of the R-factor for all models were between 0.13 and 0.98. The lowest R-factor was obtained for the HCO₃^? model (0.13) suggesting it performed well when predicting HCO₃^?. To increase the degree of objectivity in the GLUE-UA method, a set of similarity metrics, including Czekanowski, Motyka, Ruzicka, Cosine, Kumar-Hassebrook, Jaccard and Dice was applied to determine the degree of proximity and or similarity between the probability density functions of the measured and simulated water quality parameters. The measures of the similarity metrics for the HCO₃^? model were generally close to 1, indicating good performance and low uncertainty, while it showed higher uncertainty (between 0.2487 and 0.897) for the other three models (SAR, K, and TDS).