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Storytelling has been identified as an important vehicle for culture transmission. Explores the role of story creation and storytelling in culture change and culture formation. Using an anthropological approach, the research was conducted using qualitative methodology and a holistic definition of culture. Based on research in a company which had recently reorganized knowledge workers into self‐directed work teams, describes the process by which a critical incident becomes a story used to form culture. Addresses the questions: how does culture form in an organization? How can one identify its presence when one cannot assume that every grouping has culture? Can one see culture forming? What part do stories have to play in culture formation and change? Contributes to our understanding of some of the issues involved in managing self‐directed work teams.

Compared to monoracials, multiracials appear (a) to be more concerned about acceptance within their select social groups and within society at large and (b) to have higher differentiation and uniqueness needs. Artworks help consumers successfully fulfill these needs, and multiracials are heavily dependent on artworks in their (racial) identity negotiations. In addition to these needs, familial background, school, and technical qualities of artworks serve as antecedents to artwork consumption. Multiracial identity influences artwork consumption both directly and indirectly. The indirect influence is mediated by social acceptability, group identification, and uniqueness needs. Artwork consumption serves multiracials in two ways: pleasure/escape and communication/identity negotiation.

The continuous increase of energy demands is a critical worldwide matter. Jordan’s household sector accounts for 44% of overall electricity usage annually. This study aims to use artificial neural networks (ANNs) to assess and forecast electricity usage and demands in Jordan’s residential sector.

Four parameters are evaluated throughout the analysis, namely, population (P), income level (IL), electricity unit price (E$) and fuel unit price (F$). Data on electricity usage and independent factors are gathered from government and literature sources from 1985 to 2020. Several networks are analyzed and optimized for the ANN in terms of root mean square error, mean absolute percentage error and coefficient of determination (R²).

The predictions of this model are validated and compared with literature-reported models. The results of this investigation showed that the electricity demand of the Jordanian household sector is mainly driven by the population and the fuel price. Finally, time series analysis approach is incorporated to forecast the electricity demands in Jordan’s residential sector for the next decade.

The paper provides useful recommendations and suggestions for the decision-makers in the country for dynamic planning for future resource policies in the household sector.

Cost estimation is one of the most significant steps in construction planning, which must be undertaken in the preliminary stages of any project; it is required for all projects to establish the project's budget. Confidence in these initial estimates is low, primarily due to the limited availability of suitable data, which leads the construction projects to frequently end up over budget. This paper investigated the efficacy of artificial neural networks (ANNs) methodologies in overcoming cost estimation problems in the early phases of the building design process.

Cost and design data from 104 projects constructed over the past five years in Jordan were used to develop, train and test ANN models. At the detailed design stage, 53 design factors were utilized to develop the first ANN model; then the factors were reduced to 41 and were utilized to develop the second predictive model at the schematic design stage. Finally, 27 design factors available at the concept design stage were utilized for the third ANN model.

The models achieved average cost estimation accuracy of 98, 98 and 97% in the detailed, schematic and concept design stages, respectively.

This paper formulated the aims and objectives to be applicable only in Jordan using historical data of building projects.

The ANN approach introduced as a management tool is expected to provide the stakeholders in the engineering business with an indispensable tool for predicting the cost with limited data at the early stages of construction projects.

The purpose of this paper is to explore the relationship between organizational strategy, performance management and training and development in the context of the Jordanian banking sector.

Models of strategic human resource management developed in the west are considered for their relevance in Jordan. A mixed methods approach is adopted employing interviews with senior managers and training and development managers, employee questionnaires and documentary analysis. It examines all banks in Jordan including foreign and Islamic banks.

Findings indicate that training and development is not driven by human resource strategy and that it is reactive rather than proactive. Training and development does improve skills, knowledge, attitudes and behaviors but there is little evidence that it increases commitment and satisfaction nor that it contributes to strategic aims in any significant way. The linkages between strategy and training and development are not explicit and strategies are not interpreted through performance management systems. Consequently there is a lack of integration in organizational HR systems and the measurable contribution of training and development to competitive advantage is minimal

The paper offers suggestions as to how greater integration between strategy, performance management and training and development might be achieved in the Jordanian context.

This paper is the first detailed empirical study of training and development in Jordan to include considerations of transferability of western models to an Arab culture.

This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

An appropriate use of neural computing techniques is to apply them to corporate bankruptcy prediction, where conventional solutions can be hard to obtain. Having said that, choosing an appropriate Artificial Neural Network topology (ANN) for predicting corporate bankruptcy would remain a daunting prospect. The context of the problem is that there are no fixed rules in determining the ANN structure or its parameter values, a large number of ANN topologies may have to be constructed with different structures and parameters before determining an acceptable model. The trial‐and‐error process can be tedious, and the experience of the ANN user in constructing the topologies is invaluable in the search for a good model. Yet, a permanent solution does not exist. This paper identifies a non trivial novel approach for implementing artificial neural networks for the prediction of corporate bankruptcy by applying inter‐connected neural networks. The proposed approach is to produce a neural network architecture that captures the underlying characteristics of the problem domain. The research primarily employed financial data sets from the London Stock Exchange and Jordans financial database of major public and private British companies. Early results indicate that an ANN appears to outperform the traditional approach in forecasting corporate bankruptcy.

The purpose of this paper is to investigate the long-run impact of foreign aid and workers’ remittances on Jordanian economic growth using time series data for the period 1970–2014. Following the most recent literature, the author also assess whether economic policy enhances economic growth and whether aid effectiveness is conditional on levels of economic policy.

The author employs unit root tests that allow for endogenously determined structural breaks (Perron, 1997) and properly utilize the autoregressive distributed lag (ARDL) or bounds testing approach to cointegration by applying both the F- and the t-test statistics (Pesaran et al., 2001). The analysis is applied to 12 different models that incorporates the various types and sources of foreign aid.

Empirical results suggest that aid and its various components, and workers’ remittances have had a positive and significant long-run impact on economic growth. Empirical results also show: no evidence supporting the hypothesis that aid is only or more effective in spurring economic growth during periods of “good” macroeconomic policy, i.e., when Jordan has undertaken World Bank Structural Adjustment Programs (SAPs); no robust evidence supporting the World Bank’s claim that SAPs are growth enhancing. Moreover, the author found strong empirical evidence suggesting that exports and human capital are also major determinants of long-run growth in Jordan.

Although Jordan and the region at large have experienced periods of major political instability that may have had a varying impact on the economy, lack of a reliable and lengthy time series measure that accounts for political instability is not available to include in the study.

Using cointegration analysis, our empirical evidence reveals that foreign aid, labor remittances, exports and human capital have had a robust positive long-run impact on economic growth. Hence, the Jordanian government should promote policies that encourage donor countries and agencies to further extend aid to Jordan. Moreover, policies that promote exports and facilitate labor mobility to neighboring countries should also be encouraged and promoted.

Despite receiving a significant amount of foreign aid and labor remittances in the last 50 years, the author found no time series study that tested the long-run impact of these external financing sources on growth in Jordan. This study fills that gap and extends the analysis to test whether macroeconomic policy is growth enhancing and whether aid (and several of its components) are only effective or more effective in promoting growth during periods of “good” macroeconomic policy, i.e., when Jordan has undertaken a World Bank SAP.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.