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In many companies, product managers are under increasing time pressure. They are generalists in a marketing world that is increasingly specialized and complex. There are more tasks to perform, more specialist skills to acquire, more fires to fight, and less time for thinking and strategic planning. If their general management skills are to be used effectively, product managers must be able to focus their time on the tasks that exploit these skills and help their businesses to grow. The product management audit surveys product managers on how they actually spend their time and how they would ideally spend it to really build their businesses. Data from the audit can help to establish time allocation priorities for product managers and uncover potential time allocation problems before they become critical. We will first review the changes in the marketing environment that are putting pressure on the product management system. Second, we will show how any consumer, industrial, or service company can conduct a product management audit to find out how product management personnel are spending their time and why, and how satisfied they are with their jobs, the support provided, and rewards they are receiving. Third, we will illustrate the type of data that the audit can generate and present key findings from responses to audit surveys by over 300 product management personnel from 20 strategic business units in six Fortune 500 consumer goods companies. Finally, we will explain how one multidivision packaged goods company used an audit to identify problems within its product management organization and determine the actions needed to correct them.

The school timetable is a powerful administrative tool. Ideally it should operationalise the aims and objectives of the school by providing an appropriate structural dimension to the curriculum. Stark reality may prevent this ideal relationship from being achieved. Another function served by the school timetable is its allocative role. It performs the important task of allocating a large proportion of the school's resources. The resources of teacher‐time, pupil‐time and room‐space have their use controlled directly by the timetable. The material resources of equipment and supplies, which are largely related to subjects taught, are indirectly controlled. Timetable analysis can therefore serve two purposes. It can reveal the reality of a school's curriculum organization. This reality may be intentional or unintentional. Secondly, it can show where a school allocates its resources, in particular the important ones of teacher‐time and room‐space. Information in these areas should enable school administrators to make better decisions on the school's educational programme and on resource allocation practices to achieve desired aims and objectives. This paper reports part of a study which used timetable analysis for these purposes. It shows the type of information which can be obtained by such analysis and argues that the quantitative and qualitative data provided can provide a more informed basis for decision making.

This paper examines the market timing ability of a sample of 62 Australian International equity funds using the returns‐based approach of Henriksson and Merton (1981) (H&M) and Treynor and Mazuy (1966) (T&M). Specifically, the primary focus is to investigate whether market timing ability bears any relationship to the stated fund allocation policy. Generally, the results indicate that fund managers do not successfully time the market. We also find that there is no relationship between the manager's stated level of activity on allocation and their market timing abilities as calculated using the H&M and T&M models. Managers are not successfully implementing their stated policies. These results are consistent with an irrelevance of perceived management style to fund policies and hence performance. Furthermore, it is indicative that fund managers are not successfully targeting particular classes of risk averse investors.

The purpose of this paper is to study the allocation of time to paid work, unpaid work and non-work by women in Britain in 1998-1999. To infer the labor supply from the other time-use expressions.

The paper uses weekly diary data to estimate the unpaid work and non-work functions. It infers the (residual) paid work expression. As the latter is recovered from uncensored regressions, it makes direct use of the complete set of observations. Hence, it contains more information than the conventional labor supply functions that are estimated from the data obtained from paid work participants via the Tobit and Heckit or selection-bias correction (SBC) techniques.

The women surveyed generally allocated 69 percent of their time toward non-work, 18 percent to unpaid work, and 13 percent to paid work. The non-work function is dominated by the autonomous component, and all three functions depend on subjects’ age, wage, non-labor income, household composition, the unpaid work contributions of adult relatives and region of residence. The unpaid work and non-work functions are more consistent with the SBC rather than the Tobit version of the labor supply. Moreover, the Tobit predicts unrealistic paid work allocations for women engaging in very little non-work.

The unpaid work and non-work functions are regressed separately, as often the case in the literature. Their consideration within a seemingly unrelated regression framework necessitates a reduction in the number of observations to match those considered in the Tobit and SBC versions of the labor supply. Nuances may arise when the time reported in the diaries does not add up to 24 daily hours for all respondents. Knowledge of the recovered regional, age, household member and other effects on women time allocation might had come handy to economic development authorities who sought to attract women out of the household into market production, and from part-time to full-time employment in the context of the 2000-2010 Lisbon Strategy. Similar lessons may be valid today.

The data set derives from a survey that has not been used before. It relies on week-long diaries in order to avoid the occurrence of many zeros in a good number of activities (which is the norm in short diaries), and to ensure the study of a censored time-use function through its uncensored complements. The findings are compared to those of a weekly diary survey conducted in 1987 that solicited similar information. Hence, the study fills a gap in time-use analysis. Identifying the factors which influence the number of hours that women engage in work (both paid and unpaid) and non-work is useful for economic policy purposes. The study exposes a limitation in the conventional estimation of the labor supply which, in turn, casts doubt on the reliability of empirical results for policy making.

This paper aims to develop a greater understanding of international telecommunications policy diffusion through preliminary, qualitative analysis of an expected utility model. The model is tested through analyses of diffusion of spectrum license allocation policies within and between regions.

A qualitative comparative case method is used. Cases are developed from secondary data from the European Union and South America, and analyzed at the national and regional levels.

The results suggest: the expected utility model can be used for in‐depth qualitative analyses to compare effects of various diffusion mechanisms; diffusion of spectrum license allocation policies at the regional level was more strongly driven by a policy's likely effectiveness, as compared to potential payoffs for policymakers; and conversely, at the national level diffusion was driven by both payoffs for the policymakers and likely policy effectiveness.

The two academic contributions of the paper are its expansion of a unified policy diffusion model to simultaneously account for regional and national levels of governance, as well as for technological change and its application in the telecommunications domain. Practical contributions include providing a framework for systematic analysis of a telecommunications policy's benefits for the public as well as policymakers.

Credit spread change is a key issue for investors to earn the excess return from corporate bonds. Generally, there are two kinds of different effects that support the changing of credit spread: asset allocation effect and credit risk change effect. The existing literature based on US data supports credit spread change is driven by credit risk change; however, this issue based on Chinese market data has not been investigated clearly. This paper seeks to address this issue.

The authors adopt Markov regime switching model to investigate the changing mode of the credit spread in the Chinese bond market. They select three kinds of variables: the credit risk variables, the asset allocation variables and the liquidity condition variables. With ML estimators, the authors can find the changing mode and further they study the relationship between the regime switching and some observed variables, such as macro economy variables and turnover of stock market.

The authors find it is driven by asset allocation effect in most time and by credit risk change only in shorter period. Empirical results show that the switching of dominance from one effect to another isn't closely related with macro‐economy variables, but related with the turnover of stock market.

These results indicate that in China the credit risk of corporate bonds is relatively low and the corporate bonds are still auxiliary asset for investors.

In this paper, the authors have the following two contributions: first, they discuss the asset allocation effect in the Chinese bond market and introduce the stock market variables and bank credit variable to describe the asset allocation effect; second, based on Chinese bond market data, they find different findings from the existing literature about US and European bond markets, showing that the changing of credit spread is mostly related with asset allocation effect but not credit risk change.

A minimum dropout age (MDA) requires potential dropouts to stay in school until graduation. Most countries have an MDA at least 16. An MDA greater than 16 requires potential dropouts to stay in school for at least one more year, which immediately reduces their available time and opportunities to commit a crime in the community. This study aims to examine how a higher MDA reduces crime in the community. The authors then show a higher MDA helps potential dropouts to become an entrepreneur.

The authors develop an economic model of crime that shows how an MDA greater than 16 affects contemporaneous juvenile crime in the community. Considering an MDA of 16 as a benchmark MDA, a hypothetical example with simulated data on the USA is used. The authors then show how a higher MDA offers a financial opportunity for the professional development programs.

An MDA greater than 16 reduces crime in the community. Reducing crime allows preventing social and monetary cost on juvenile delinquency. This economic efficiency offers a financial ability for adolescent training and other development programs and thereby reduces unemployment and other adverse consequences of the society.

Unlike previous studies, the authors develop an economic model of crime that shows a hypothetical relationship between an MDA and contemporaneous juvenile crime in the community. A higher MDA allows more financial ability for juvenile development programs in high school to improve the entrepreneurial skills.

Software testing is usually a very costly and time‐consuming phase in software development. As most software systems are modular, it is of great importance for the management to allocate the limited testing‐time among the software modules in an optimal way so that the highest quality and reliability of the complete system can be achieved. In this paper, the problem of optimal testing‐time allocation for modular software systems is studied. A generic formulation of the problem is presented based on nonhomogeneous Poisson process models. The aim is to maximize the operational reliability of the software system. Numerical examples are presented to illustrate the optimisation algorithm and the solution. Furthermore, as software reliability growth models consist of a number of parameters, an example of a sensitivity analysis is also shown. Such a sensitivity study is useful as important model parameters can be identified given more attention.