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The purpose of this paper is to associate a probabilistic confidence with the stock market interval forecasts obtained with the interval least squares (ILS) algorithm. The term probabilistic confidence in this paper means the probability of a point observation that will fall in the interval forecast.

Using confidence interval as input, annual ILS forecasts of the stock market were made. Then the probability of point observation that fall in the annual forecasts was examined empirically.

When using confidence interval as ILS input, the stock market annual interval forecasts may have the same level of confidence as that of the input intervals.

At the same confidence level, the ILS can produce much better quality forecasts than the traditional ordinary least squares method for the stock market. Although the algorithmic approach can be applied to analyze other datasets, one should examine implications of computational results as always.

Results of this specific paper may be interesting to executive officers, other financial decision makers and to investors.

Although the ILS algorithm has been recently developed in forecasting the variability of the stock market, this paper presents the first successful attempt in associating a probabilistic confidence with ILS interval forecasts.

The purpose of this paper is to forecast variability in mortgage rates by using interval measured data and interval computing method.

Variability (interval) forecasts generated by the interval computing are compared with lower‐ and upper‐bound forecasts based on the ordinary least squares (OLS) rolling regressions.

On average, 56 per cent of annual changes in mortgage rates may be predicted by OLS lower‐ and upper‐bound forecasts while the interval method improves forecasting accuracy to 72 per cent.

This paper uses the interval computing method to forecast variability in mortgage rates. Future studies may expand variability forecasting into more risk‐managing areas.

Results of this study may be interesting to executive officers of banks, mortgage companies, and insurance companies, builders, investors, and other financial decision makers with an interest in mortgage rates.

Although it is well‐known that changes in mortgage rates can significantly affect the housing market and economy, there is not much serious research that attempts to forecast variability in mortgage rates in the literature. This study is the first endeavor in variability forecasting for mortgage rates.

The purpose of this study is to investigate the impacts of interval measured data, rather than traditional point data, on economic variability studies.

The study uses interval measured data to forecast the variability of future stock market changes. The variability (interval) forecasts are then compared with point data‐based confidence interval forecasts.

Using interval measured data in stock market variability forecasting can significantly increase forecasting accuracy, compared with using traditional point data.

An interval forecast for stock prices essentially consists of predicted levels and a predicted variability which can reduce perceived uncertainty or risk embedded in future investments, and therefore, may influence required returns and capital asset prices.

This paper aims to investigate the preparation of Nano-Al₂O₃ lubricant, as well as the effect of surface modification of Al₂O₃ on friction and wear properties.

The chemical parameters such as the energy levels of the Highest Occupied Molecular Orbital, the Lowest Unoccupied Molecular Orbital and the Fukui indices of seven modifiers, including myristic acid myristyl ester, glycerol trioleate, acetyl monoethanolamine, docosanamide, Tween-60, dodecyl dihydroxyethyl amine oxide and sodium dodecyl sulfate (SDS), are calculated by using the Materials Studio software. Meanwhile, the adsorption energies of these seven modifiers on Al₂O₃ nanoparticles are also calculated. Based on the simulation results, SDS and Tween-60 were identified as the most effective surface modifiers. Modified lubricants were prepared with Al₂O₃ nanoparticles at varying concentrations (0.1–0.4 Wt.%). Their tribological properties, including the maximum nonseizure load (P_B) and the coefficient of friction (COF), were evaluated using a four-ball wear tester. The worn surfaces were analyzed by scanning electron microscopy and three-dimensional profilometry.

The results indicate that SDS improves both the extreme pressure and anti-wear performance of the lubricant. The lubricant achieves optimal performance when combined with 1.0 Wt.% SDS and 0.2 Wt.% nano-Al₂O₃. At this combination, the value of PB reaches 209 N, and the value of COF is approximately 0.072. Compared to the unmodified Al₂O₃ lubricant with a COF of 0.086, this represents a 23% reduction in COF.

Modified Al₂O₃ lubricants demonstrate superior lubrication performance and effectively reduce the COF, providing valuable insights for the practical application of nanolubricants.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2024-0353/

The purpose of this paper is to propose a research model in which coworker service sabotage influences hospitality employees’ service creativity via work engagement. It also aims to test the moderating effect of sensitivity to the interpersonal mistreatment of others (SIMO).

A time-lagged questionnaire study was performed in hotels in China. The hypotheses were tested via hierarchical multiple regression.

Coworker service sabotage is indirectly associated with hospitality employees’ service creativity via work engagement. The trait of SIMO buffers the harmful effect of coworker service sabotage.

Although our research design helps mitigate common method bias, it could still exist. Other coworker behaviors that might influence employees were not included in this research. The findings may also be biased due to the restricted sample from China.

Hospitality organizations should take measures to curb service sabotage. Organizations could also provide supportive resources to suppress the negative impacts of coworker service sabotage. Moreover, organizations should motivate those low in SIMO to care more about customers.

The research takes the lead in investigating the outcomes of service sabotage from a third-party perspective. Work engagement is identified as the mechanism for transmitting the impact of coworker service sabotage to employees. Moreover, a new moderator that attenuates the negative effects of coworker service sabotage is found.