Consensual commitment: a grounded theory of the meso-level influence of organizational design on leadership and decision-making

This research examined the relationship between organizational design and leadership in decision-making teams. It used a grounded theory-based qualitative research design. The validity of the research was enhanced by data triangulation, wherein quantitative psychometric data augmented the qualitative data that are traditionally used. The research was based upon two organizations within the substantive setting of the knowledge industry. The higher order category of consensual commitment explained effective decision-making. At the meso-level of leadership modeling, organizational design influenced both leadership style and decision-making. Specifically, an organizational design that generated lateral job roles and a relational leadership orientation was found to enhance consensual commitment, and provided a level of assurance against dysfunctional team dynamics. © 2009 Elsevier Inc. All rights reserved.

A panel vector error correction approach to forecasting demand in regional construction markets

Reliable forecasting as to the level of aggregate demand for construction is of vital importance to developers, builders and policymakers. Previous construction demand forecasting studies mainly focused on temporal estimating using national aggregate data. The construction market can be better represented by a group of interconnected regions or local markets rather than a national aggregate, and yet regional forecasting techniques have rarely been applied. Furthermore, limited research has applied regional variations in construction markets to construction demand modelling and forecasting. A new comprehensive method is used, a panel vector error correction approach, to forecast regional construction demand using Australia’s state-level data. The links between regional construction demand and general economic indicators are investigated by panel cointegration and causality analysis. The empirical results suggest that both long-run and causal links are found between regional construction demand and construction price, state income, population, unemployment rates and interest rates. The panel vector error correction model can provide reliable and robust forecasting with less than 10% of the mean absolute percentage error for a medium-term trend of regional construction demand and outperforms the conventional forecasting models (panel multiple regression and time series multiple regression model). The key macroeconomic factors of construction demand variations across regions in Australia are also presented. The findings and robust econometric techniques used are valuable to construction economists in examining future construction markets at a regional level.

Workplace spirituality and organizational commitment influence on job performance among academic staff

This study examines the relationships between spirituality in the workplace, organizational commitment and job performance measured in terms of key performance indicators (KPIs) based on a sample of 376 academic staff at Universiti Sains Malaysia (USM). The methods used in the study are factor analysis and multiple regression analysis. Three factors are found to explain organizational commitment: affective commitment, continuance commitment and normative commitment. Affective and normative commitments are positively influenced by workplace spirituality, which is explained by three factors: alignment between organizational and individual values; sense of enjoyment at work and contribution to community; and opportunity for inner life. The study also finds that neither high commitment nor workplace spirituality among academic staff necessarily manifest in high KPIs. Instead, other staff background variables appear to have more influence on job performance, such as gender, stream, age and rank.

Effects of type reduction algorithms on forecasting accuracy of IT2FLS models

Type reduction (TR) is one of the key components of interval type-2 fuzzy logic systems (IT2FLSs). Minimizing the computational requirements has been one of the key design criteria for developing TR algorithms. Often researchers give more rewards to computationally less expensive TR algorithms. This paper evaluates and compares five frequently used TR algorithms based on their contribution to the forecasting performance of IT2FLS models. Algorithms are judged based on the generalization power of IT2FLS models developed using them. Synthetic and real world case studies with different levels of uncertainty are considered to examine effects of TR algorithms on forecasts' accuracies. As per obtained results, Coupland-Jonh TR algorithm leads to models with a higher and more stable forecasting performance. However, there is no obvious and consistent relationship between the widths of the type reduced set and the TR algorithm. © 2013 Elsevier B.V.

Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.

Electricity load and price forecasting with influential factors in a deregulated power industry

With the emergence of smart power grid and distributed generation technologies in recent years, there is need to introduce new advanced models for forecasting. Electricity load and price forecasts are two primary factors needed in a deregulated power industry. The performances of the demand response programs are likely to be deteriorated in the absence of accurate load and price forecasting. Electricity generation companies, system operators, and consumers are highly reliant on the accuracy of the forecasting models. However, historical prices from the financial market, weekly price/load information, historical loads and day type are some of the explanatory factors that affect the accuracy of the forecasting. In this paper, a neural network (NN) model that considers different influential factors as feedback to the model is presented. This model is implemented with historical data from the ISO New England. It is observed during experiments that price forecasting is more complicated and hence less accurate than the load forecasting.

Organizational Fit: Key Issues and New Directions

An ambitious survey of the field, by an international group of scholars, that looks toward the future of person-organization fit. Explores how people form their impressions of fit and the impact these have on their behavior, and how companies can maximize fit. Includes multiple perspectives on the topic of how people fit into organizations, discussing issues across the field and incorporating insights from related disciplines. Actively encourages scholars to take part in organizational fit research, drawing on workshops and symposia held specially for this book to explore some of the creative directions that the field is taking into the future. © 2013 John Wiley & Sons, Ltd.

Economic Bias of Weather Forecasting: A Spatial Modeling Approach

The value of accurate weather forecast information is substantial. In this paper we examine competition among forecast providers and its implications for the quality of forecasts. A simple economic model shows that an economic bias geographical inequality in forecast accuracy arises due to the extent of the market. Using the unique data on daily high temperature forecasts for 704 U.S. cities, we find that forecast accuracy increases with population and income. Furthermore, the economic bias gets larger when the day of forecasting is closer to the target day; i.e. when people are more concerned about the quality of forecasts. The results hold even after we control for location-specific heterogeneity and difficulty of forecasting.