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The purpose of this paper is to determine the properties of analysts’ cash flows from operations (CFO) forecast generated for Australian listed firms as a productive activity, within the wider processes of financial disclosure in Australia.

Two categories of criteria are adopted: first, basic predictive statistical performance relative to a benchmark model and earnings forecasts; and second, relevance for equity pricing, as indicated by the market reaction to cash flow or forecast error reactions. The final sample comprised 2,138 observations between 2001 and 2016 and several regression models are estimated to determine the relative performance and market reaction.

Analysts’ consensus cash flow forecasts demonstrate poor predictive performance relative to earnings forecasts. Cash flow forecasts are typically naïve extensions of earnings forecasts. Furthermore, cash flow forecasts appear to be of minimal use for equity market participants in complementing earnings forecasts’ role in informing firms’ equity pricing.

While analysts’ earnings forecasts are useful for making predictions, the role of analysts’ cash flow forecasts in capital market functional efficiency appears quite limited.

This study is one of few that examines comparative usefulness of analysts’ earnings and cash flow forecasts and their predictive power using the Australian setting. Additionally, it enriches the sparse international literature on such forecasts.

Purchasing effectiveness is one of the most critical factors in determining the profitability of business, and yet it is typically one of the least well understood and managed. In a recent study with 50 leading European businesses we have established that an effective purchasing strategy can improve profitability by up to 30 per cent. Few businesses seriously assess their purchasing activity or invest in its development, so that usually this source of value remains untapped.

Quality management has come a long way since the obsession with total quality management of the 1980s. Industry leaders and “management thinkers” have increasingly focused on quality initiatives in terms of customer expectations rather than suppliers’ measures. This has helped to ensure that investment in new products and services is market led, which in turn leads to better overall business performance.

This chapter addresses adaptation to dynamic, novel and uncertain military environments. These environments require a shift from a maneuver warfare paradigm to an asymmetric world where shifting alliances, questionable civilian loyalties, opaque cultures, and the requirement to maintain peace one day and combat the next makes for a particularly confusing situation. This new warfare paradigm requires adaptation to an uncertain, complex environment.

The initial section discusses a general cognitive model of visualization called RAVENS and its importance for adaptation developed specifically to address complex military environments. RAVENS posits that humans are inherently flexible decision makers and situation awareness depends on the ability of humans to create narrative visualizations that capture the overall context of complex military environments. Using the framework as a guideline, we will examine two important visualization research programs whose purpose is to allow military operators to rapidly adapt to volatile situations. The first program investigates cognitive effects such as the framing bias and their possible interactions with a variety of display concepts during a series of missile defense simulations. The experimenters presented risk as a spatial representation of uncertainty and target value that emphasized either expected population lost or expected population saved. The second program investigated the feasibility of using visualizations generated from Scheherazade (a coevolutionary algorithm) to aid MI analysts in predicting emergent tactics of terrorist groups during urban operations. Finally, we discuss the value of these approaches for providing coherent narrative understanding as called for in the RAVENS model.

The purpose of this paper is to assess the financial disclosure vis‐á‐vis economic reality of research and development (R&D) expensed by Australian firms under the pre‐2005 Australian generally accepted accounting principles (A‐GAAP) regime via the lens of market‐to‐book.

The authors estimated firms' R&D profit rate, measured R&D revenue intensity and modelled the impacts of these and related economic factors, via economic and financial disclosure channels, on market‐to‐book using data for 1988‐2004.

R&D, on average, was profit neutral and had undetectable impacts on market‐to‐book whether via equity valuation or financial disclosure.

Market‐to‐book's information content is best viewed as conditional on the reference disclosure regime. Australian firms' typically at best minimal R&D profitability is an international anomaly. Data limitations in terms of the generating process and availability mean that R&D's impact on market‐to‐book via financial reporting is not definitively determined.

Restrictive rules on the capitalization of intangible asset‐related expenditures under A‐GAAP apparently did not adversely impact market‐to‐book's economic information. AIFRS's more permissive rule risks compromising market‐to‐book's reliability in such a role.

For Australia, the paper is anticipated to be the first to estimate the profit rate of R&D, measure the intensity of R&D, and model R&D's influence on the market‐to‐book ratio. It develops a framework for the economic and financial reporting impacts of investments on a key indicator of firms' financial standing and contributes to the debate on identifiable intangibles' disclosure.

Demand for goods/services has increased in Latin America due to urbanization, leading to a complex delivery system and increased logistical activities. In Quito, the Historic Center and La Mariscal are two zones that face logistical challenges. The objective of this chapter is to analyze the commercial logistic activities related to loading and unloading goods in these zones. To address this urban freight problem, this chapter proposes a solution through the calculation of the optimal number and location of loading and unloading bays in each zone based on actual commercial activity data. First, a delivery survey was completed in each zone regarding frequency and amount of deliveries. Then, based on the data obtained, an optimization model is proposed to determine the optimal number and location of loading and unloading bays. Finally, a simulation model of the delivery process is performed to readjust the bay’s optimal number. A total number of 75 and 98 bays were calculated to serve the total shopping district of a representative square kilometer (km²) of the Historic Center and La Mariscal. This solution aims to minimize the delivery time and the distance for deliveries, improve urban freight transportation, and reduce traffic. This study could be used as a baseline and guide for further research in urban logistics, especially in Latin America, where urban logistics is still under study. This chapter is part of a Research Project of Urban Logistics in Quito, led by Universidad San Francisco de Quito (USFQ), in association with the Megacity Logistics Lab of MIT.

The purpose of this study is to investigate the factors which might affect the intention to use interactive whiteboards (IWBs) by university students, using Technology Acceptance Model by the structural equation modeling approach. The following hypothesis guided the current study: H1. There is a positive relationship between IWB self-efficacy and perceived learning (PL). H2. There is a positive relationship between IWB self-efficacy and perceived ease of use (PEOU). H3. There is a positive relationship between IWB self-efficacy and perceived usefulness (PU). H4. There is a positive relationship between PL and PEOU. H5. There is a positive relationship between PL and PU. H6. There is a positive relationship between PEOU and attitude. H7. There is a positive relationship between PU and attitude.

A survey method was used to collect the data. Purposive sampling was used, and 416 high-school students participated in the current study. Descriptive statistics and structural equation model conducted to test the proposed model were used in data analysis.

All hypotheses formulated within the scope of the research model were statistically confirmed. H1, which assumed that interactive whiteboard self-efficacy (IWBSE) have had a positive impact on PL, was found to be statistically significant. The impact of IWBSE on PL was 0.61 which means that if IWBSE increases one unit, the impact on PL will be an average of 0.61 points. The relationship between IWBSE, which is expressed in H2 and H3, and, respectively, PEOU and PU latent variables, was statistically significant. IWBSE mostly affected PEOU (= 0.51, t = 7.92), and then PU (= 0.16, t = 2.43). The relationship between PL – which is expressed in H4 and H5– and, respectively, PEOU and PU latent variables, was statistically significant. IWBSE mostly affected PU (= 0.63, t = 7.94), and then PEOU (= 0.27, t = 3.95).

It is aimed to transform the entire classes into computerized classes (Smart Class) around Turkey with FATIH project which includes usage of interactive boards. Because it is a very big project funded by the government, it is important to evaluate its adaptation among teachers and students. Therefore, the current study aims to investigate the factors which might affect the attitude to use IWBs by high-school students.

The first in a series of articles, explains the background to the establishment of the Training and Development Lead Body standards and introduces two organizations which are adopting the TDLB standards. The organizations concerned are the Man‐made Fibres Industry Training Advisory Board and the London and South East Region Advisory Council for Education and Training.

Explains the background to the establishment of the Training and Development Lead Body standards and introduces two organizations which are adopting the TDLB standards. The organizations concerned are the Man‐made Fibres Industry Training Advisory Board and the London and South East Region Advisory Council for Education and Training.