Search results

This paper aims to investigate the internet web page metadata usage behavior in terms of their metadata element co‐occurrences. Metadata are designed to facilitate both web publishers/authors to organize their web pages and search engines to index the web pages accurately.

This study examines the types of metadata elements employed by different professional groups of web authors, the number of elements they prefer to use, and the types of element combinations they typically embed in their pages' HTML code.

The findings reveal that the “keyword” and “description” elements were the most popular single elements. The most popular combination of two elements was that of “keyword and description”. Very few authors included combinations of five elements. This study also shows that preferences for element combinations varied by domains.

This approach will enhance the current understanding of metadata usage behavior and may help search engine designers as they continue their quest for improved indexing and retrieval of web pages.

The author defines and discusses the three laws of business combinations that are essential to a profitable use of resources.

The author shows how applying these laws is necessary for success.

All business combinations must have the potential to create joint value, must be governed to realize this value, and must share value in a way that provides a reward to each party’s investment

In remix strategy, the fundamental unit of analysis is the combination of resources that yields value. That combination competes with other combinations. Some combinations will gain advantage over others because they encompass just the right resources; others will gain advantage because they manage their collective resources better than others do.

The author’s insight is that instead seeing competition as a battle of firm vs. firm, practitioners need to understand how bundles of resources compete, regardless of whether they are organized as firms.

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.

In 2009 and 2010, the Financial Accounting Standards Board (FASB) adopted new accounting standards for nonprofit mergers and acquisitions. The new accounting standards are an example of the constitutive role accounting can play in how people think about economic events, since the FASB defined a new concept (the “inherent contribution”) and required valuation of intangible assets that were often previously unrecognized.

The FASB’s stated goals included minimizing “pooling” accounting and maximizing transparency regarding fair value information, acquired identifiable intangible assets, and the relation between consideration paid and the fair values of identifiable assets acquired. The FASB expected many combinations would involve little or no consideration. It also expressed concern that some organizations would undervalue assets acquired, especially intangible assets.

For a sample of 2012–2017 nonprofit hospital combinations, we find general agreement with the FASB’s expectations. Almost all combinations were accounted for as acquisitions, not mergers, even though there was frequently no consideration paid. More acquirers recorded “inherent contributions” than goodwill, because the net fair value of the acquired hospital’s identifiable assets exceeded the consideration paid. Acquirers ascribed value to assets, such as intangible assets, that would have gone unreported under the prior accounting rules, although lower levels of intangible assets were recognized in nonprofit business combinations, relative to total non-goodwill assets acquired, than in public companies’ acquisitions.

The authors develop a novel forecast combination approach based on the order statistics of individual predictability from panel data forecasts. To this end, the authors define the notion of forecast depth, which provides a ranking among different forecasts based on their normalized forecast errors during the training period. The forecast combination is in the form of a depth-weighted trimmed mean. The authors derive the limiting distribution of the depth-weighted forecast combination, based on which the authors can readily construct prediction intervals. Using this novel forecast combination, the authors predict the national level of new COVID-19 cases in the United States and compare it with other approaches including the ensemble forecast from the Centers for Disease Control and Prevention (CDC). The authors find that the depth-weighted forecast combination yields more accurate and robust predictions compared with other popular forecast combinations and reports much narrower prediction intervals.

Our meta-analysis of 92 international samples, with a total sample size of 8,491, demonstrates that firms following internationalization strategies by means of external growth modes can realize a significant positive performance impact on firm performance (r=0.156). This performance effect is significantly stronger than for firms using external growth strategies in their home country (117 samples, with a total sample size of 29,998, r=0.077). Moderating effects are found for the type of international business combination (mergers and acquisitions versus alliance) and the internationalizing firm's region of origin, whereas the relatedness of the firms and the region entered show no moderating impact.

This paper aims to present a set of processes for obtaining the global spraying trajectory of a cold spraying robot on a complex surface.

The complex workpiece surfaces in the project are first divided by triangular meshing. Then, the geodesic curve method is applied for local path planning. Finally, the subsurface trajectory combination optimization problem is modeled as a GTSP problem and solved by the ant colony algorithm, where the evaluation scores and the uniform design method are used to determine the optimal parameter combination of the algorithm. A global optimized spraying trajectory is thus obtained.

The simulation results show that the proposed processes can achieve the shortest global spraying trajectory. Moreover, the cold spraying experiment on the IRB4600 six-joint robot verifies that the spraying trajectory obtained by the processes can ensure a uniform coating thickness.

The proposed processes address the issue of different parameter combinations, leading to different results when using the ant colony algorithm. The two methods for obtaining the optimal parameter combinations can solve this problem quickly and effectively, and guarantee that the processes obtain the optimal global spraying trajectory.

Tourism demand forecasting is vital for the airline industry and tourism sector. Combination forecasting has the advantage of fusing several forecasts to reduce the risk of inappropriate model selection for analyzing decisions. This paper investigated the effects of a time-varying weighting strategy on the performance of linear and nonlinear forecast combinations in the context of tourism.

This study used grey prediction models, which did not require that the available data satisfy statistical assumptions, to generate forecasts. A quality-control technique was applied to determine when to change the combination weights to generate combined forecasts by using linear and nonlinear methods.

The empirical results showed that except for when the Choquet fuzzy integral was used, forecast combination with time-varying weights did not significantly outperform that with fixed weights. The Choquet integral with time-varying weights significantly outperformed that with fixed weights for all model combinations, and had a superior forecasting accuracy to those of other combination methods.

The tourism sector can benefit from the use of the Choquet integral with time-varying weights, by using it to formulate suitable strategies for tourist destinations.

Combining forecasts with time-varying weights may improve the accuracy of the predictions. This study investigated incorporating a time-varying weighting strategy into combination forecasting by using CUSUM. The results verified the effectiveness of the time-varying Choquet integral for tourism forecast combination.

Forecasting tourism demand accurately can help private and public sector formulate strategic planning. Combining forecasting is feasible to improving the forecasting accuracy. This paper aims to apply multiple attribute decision-making (MADM) methods to develop new combination forecasting methods.

Grey relational analysis (GRA) is applied to assess weights for individual constituents, and the Choquet fuzzy integral is employed to nonlinearly synthesize individual forecasts from single grey models, which are not required to follow any statistical property, into a composite forecast.

The empirical results indicate that the proposed method shows the superiority in mean accuracy over the other combination methods considered.

For tourism practitioners who have no experience of using grey prediction, the proposed methods can help them avoid the risk of forecasting failure arising from wrong selection of one single grey model. The experimental results demonstrated the high applicability of the proposed nonadditive combination method for tourism demand forecasting.

By treating both weight assessment and forecast combination as MADM problems in the tourism context, this research investigates the incorporation of MADM methods into combination forecasting by developing weighting schemes with GRA and nonadditive forecast combination with the fuzzy integral.