Improving the accuracy and operational predictability of project cost forecasts: an adaptive combination approach

Conducting an early warning forecast to detect potential cost overrun is essential for timely and effective decision-making in project control. This paper presents a forecast combination model that adaptively identifies the best forecast and optimises various combinations of commonly used project cost forecasting models. To do so, a forecast error simulator is formulated to visualise and quantify likely error profiles of forecast models and their combinations. The adaptive cost combination (ACC) model was applied to a pilot project for numerical illustration as well as to real world projects for practical implementation. The results provide three valuable insights into more effective project control and forecasting. First, the best forecasting model may change in individual projects according to the project progress and the management priority (i.e. accuracy, outperformance or large errors). Second, adaptive combination of simple, index-based forecasts tends to improve forecast accuracy, while mitigating the risk of large errors. Third, a post-mortem analysis of seven real projects indicated that the simple average of two most commonly used cost forecasts can be 31.2% more accurate, on average, than the most accurate alternative forecasts.     

An improved meta-analysis for analyzing cylindrical-type time series data with applications to forecasting problem in environmental study

In this paper, we propose an improved generalized least square (GLS) meta-analysis in a linear-circular regression, and show its utility in the analysis of a certain environmental issue. The existing GLS meta-analysis proposed in Becker and Wu has a serious flaw since information about the covariance among coefficients across studies is not utilized. In our proposed meta-analysis, we take the correlations between adjacent studies into account, and improve the existing GLS meta-analysis. We provide numerical examples to compare the proposed method with several other existing methods by using Akaike's Information Criterion, Bayesian Information Criterion and mean square prediction errors with applications to forecasting problem in Environmental study.     

A general algorithm for non-parametric maximum likelihood estimator of stochastically ordered survival functions from case 2 interval-censored data

In this paper, we study an algorithm to compute the non-parametric maximum likelihood estimator of stochastically ordered survival functions from case 2 interval-censored data. The algorithm, simply denoted by SQP (sequential quadratic programming), re-parameterizes the likelihood function to make the order constraints as a set of linear constraints, approximates the log-likelihood function as a quadratic function, and updates the estimate by solving a quadratic programming. We particularly consider two stochastic orderings, simple and uniform orderings, although the algorithm can also be applied to many other stochastic orderings. We illustrate the algorithm using the breast cancer data reported in Finkelstein and Wolfe (1985 Finkelstein, D. M., and R. A. Wolfe. 1985. A semiparametric model for regression analysis of interval-censored failure time data. Biometrics 41:933–45. [Google Scholar]).     

Regression analysis of recurrent events data with incomplete observation gaps

For analyzing recurrent event data, either total time scale or gap time scale is adopted according to research interest. In particular, gap time scale is known to be more appropriate for modeling a renewal process. In this paper, we adopt gap time scale to analyze recurrent event data with repeated observation gaps which cannot be observed completely because of unknown termination times of observation gaps. In order to estimate termination times, interval-censored mechanism is applied. Simulation studies are done to compare the suggested methods with the unadjusted method ignoring incomplete observation gaps. As a real example, conviction data set with suspensions is analyzed with suggested methods.     

Examining the Relationship between Civil Servant Perceptions of Organizational Culture and Job Attitudes: in the Context of the New Public Management Reform in South Korea

This study investigates the relationship between public officials’ perceptions of organizational culture and their job attitudes, particularly emphasizing a mediating role of job satisfaction under the new public management reform in South Korea. Data collected from Korean civil servants indicate that perceptions of the competing values rooted in different organizational culture types—clan, market, hierarchy, and adhocracy—differentially affect their job attitudes. In addition, the findings show the mediating influence of job satisfaction between public officials’ perceptions of organizational culture and organizational commitment.     

Women's Attitudes Toward Family Formation and Life Stage Transitions: A Longitudinal Study in Korea

Although it is well documented that family attitudes become less traditional over cohorts, little is known about how individuals' attitudes change over time. More research also is needed on how the within‐individual changes relate to important life stage events such as marriage, childbirth, and transitions in education and work. Evidence is particularly lacking in Asian countries, which have socioeconomic and cultural contexts very different from those in the West. To fill these gaps in the literature, the authors analyzed the attitudes toward family formation of Korean women in their 20s and 30s (N = 6,042). Individual fixed effects regression using the panel data from the Korean Longitudinal Survey of Women and Families revealed that women became more traditional over time and that transitions to marriage and motherhood partly accounted for the change. These findings are explained within the context of very low fertility in Korea and have implications for other rapidly changing societies.     

A 25-year perspective on levels of analysis in leadership research

The purpose of this article is to present a comprehensive 25-year review of the incorporation of levels of analysis into conceptual and empirical leadership research published within Leadership Quarterly throughout its history. We assessed the population of Leadership Quarterly's research (790 research articles) on four key levels of analysis-based issues: (1) explicit statement of the focal level(s) of analysis; (2) appropriate measurement given level of constructs; (3) use of a multi-level data analysis technique; and, (4) alignment of theory and data. Prior reviews regarding levels of analysis incorporation into leadership research have been limited to major research domains. Results revealed that while both conceptual and empirical articles only explicitly state the focal level of analysis in approximately one-third of the articles, appropriate levels-based measurement and alignment between theory and data are relatively strong areas of achievement for the articles within Leadership Quarterly. Multi-level data analysis techniques are used in less than one-fifth of all articles. Although there is room for improvement, there is evidence that Leadership Quarterly is a premier outlet for levels-based leadership research. Given the increasing complexity of organizational science with regard to groups, teams and collectives, Leadership Quarterly has an opportunity to model for organizational research on how to build and test complicated multi-level theories and models.     

Leadership as social identity management: Introducing the Identity Leadership Inventory (ILI) to assess and validate a four-dimensional model

Although nearly two decades of research have provided support for the social identity approach to leadership, most previous work has focused on leaders' identity prototypicality while neglecting the assessment of other equally important dimensions of social identity management. However, recent theoretical developments have argued that in order to mobilize and direct followers' energies, leaders need not only to ‘be one of us’ (identity prototypicality), but also to ‘do it for us’ (identity advancement), to ‘craft a sense of us’ (identity entrepreneurship), and to ‘embed a sense of us’ (identity impresarioship). In the present research we develop and validate an Identity Leadership Inventory (ILI) that assesses these dimensions in different contexts and with diverse samples from the US, China, and Belgium. Study 1 demonstrates that the scale has content validity such that the items meaningfully differentiate between the four dimensions. Studies 2, 3, and 4 provide evidence for the scale's construct validity (distinguishing between dimensions), discriminant validity (distinguishing identity leadership from authentic leadership, leaders' charisma, and perceived leader quality), and criterion validity (relating the ILI to key leadership outcomes). We conclude that by assessing multiple facets of leaders' social identity management the ILI has significant utility for both theory and practice.     

Anti-Corruption Initiatives and E-Government: A Cross-National Study

This paper discusses various approaches of anti-corruption initiatives and conducts statistical analyses on aggregate data of more than 200 different countries to find relationships between e-government and anti-corruption in government. Findings indicate that e-government could be an effective tool to curb corruption in government in spite of the fact that the rule of law is the most powerful predictor of anti-corruption as well as a fundamental precondition of a clean government. Findings also indicate that if digital government is strengthened with high-quality public bureaucracies, anti-corruption efforts could more effectively lower the corruption levels of public affairs.