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The incorporation of phosphide additives is regarded as a highly effective strategy for enhancing the lubricative qualities of base oils. This study aims to assess the lubrication behavior and efficacy of various phosphide additives in polyethylsiloxane (PES) through the employment of the Schwingum Reibung Verschleiss test methodology, across a temperature range from ambient to 300°C.

PES demonstrated commendable lubrication capabilities within the Si3N4/M50 system, primarily attributable to the Si-O frictional reaction film at the interface. This film undergoes disintegration as the temperature escalates, leading to heightened wear. Moreover, the phosphide additives were found to ameliorate the issues encountered by PES in the Si3N4/M50 system, characterized by numerous boundary lubrication failure instances. A chemical film comprising P-Fe-O was observed to form at the interface; however, at elevated temperatures, disintegration of some phosphide films precipitated lubrication failures, as evidenced by a precipitous rise in the coefficient of friction.

The results show that a phosphide reactive film can be formed and a reduction in wear rate is achieved, which is reduced by 64.7% from 2.98 (for pure PES at 300°C) to 1.05 × 10^–9 μm³/N m (for triphenyl phosphite at 300°C).

The data derived from this investigation offer critical insights for the selection and deployment of phosphide additives within high-temperature lubrication environments pertinent to PES.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0139/

This paper aims to study the corrosion inhibition behavior of imidazopyridine and its three derivatives on brass.

The authors performed weight loss experiments, electrochemical experiments including the polarization curve and electrochemical impedance spectrum, corrosion morphology observation using scanning electron microscope (SEM) and atomic force microscope (AFM) and surface composition analysis via X-ray photoelectron spectroscopy (XPS) to analyze the corrosion inhibition behavior of imidazopyridine and its three derivatives on brass by using quantum chemical calculation (Gaussian 09), molecular dynamics simulation (M-S) and Langmuir adsorption isotherm.

According to the results, imidazole-pyridine and its derivatives were found to be modest or moderately mixed corrosion inhibitors; moreover, they were spontaneously adsorbed on the metal surface in a single-layer, mixed adsorption mode.

The corrosion inhibition properties of pyrazolo-[1,2-a]pyridine and its derivatives on brass in sulfuric acid solution were analyzed through weight loss and electrochemical experiments. Moreover, SEM and AFM were simultaneously used to observe the corrosion appearance. Furthermore, XPS was used to analyze the surface. Then, Gaussian 09 and M-S were combined along with the Langmuir adsorption isotherm to investigate the corrosion inhibition mechanism of imidazole-[1,2-a]pyridine and its derivatives.

Contractual services are characterized by features such as termination fees, long-term commitment, and complex terms. When customers find better deals from other providers, they may want to switch, but contractual obligations prevent them from doing so. Thus, this study aims to draw upon the stimulus-organism-response paradigm and theories of emotion regulation to examine how punitive switching costs (PSCs) can evoke negative emotions (NEs) from customers and, consequently, lead to negative behavioral responses in contractual service settings.

This study collected data from 395 customers of telecommunications companies, fitness centers, tutoring firms/centers, and house leasing companies in Taiwan. We tested the hypotheses using partial least squares structural equation modeling via SmartPLS 3.0.

The results show that NEs partially mediate the relationship between PSC and customers’ switching intention and negative word-of-mouth. This study also finds alternative attractiveness (AA) and service recovery (SR) do not moderate the PSCs–NEs relationship, but AA does directly influence NEs.

First, this study contributes to the literature on switching costs by exploring how PSC exerts a detrimental impact on behavioral responses. Second, this study adds to the literature on service failures by identifying the mediating role of NEs in such a relationship.

This study aims to explore the mediating effect of digital options on the relationship between emerging information technology investments (ITIs) and firm performance (FP). In particular, it analyses the performance impacts of investments in five emerging technologies of IT or non-IT firms.

Secondary data are collected from Chinese A-share listed companies from 2010 to 2018. The authors propose an econometric model focusing on the impact of ITIs on a firm’s market value and profit. A propensity score matching model is applied to control endogeneity.

The ITIs’ effect on FP is found to be completely mediated by digital options, and the reach of digital options plays a more positive role in the relationship between ITIs and Tobin’s Q, whereas the richness of digital options is stronger between ITIs and return on net assets (ROE). The group study shows that the impact of process technologies such as cloud computing and the Internet of Things has a more profound impact on Tobin’s Q, and the knowledge technologies represented by artificial intelligence, blockchain and big data strongly affect ROE. In addition, the positive relationship between ITIs and FP is unrelated to IT/non-IT firms.

First, the data are based on 219 publicly announced emerging ITIs in China and thus may not be generalizable to other cultural/national contexts. Second, there is a lack of a large sample data set of emerging ITI information in China, and the duration of this study is constrained to the relatively short rise of emerging technologies.

This study provides firm decision-makers with practical implications. The results imply that the effect of ITIs on FP depends on digital options, so both IT firms (e.g., Big Tech giants) and non-IT firms (e.g., incumbents) should discover how to balance firm value and profit in their management of emerging technology investment projects with digital options thinking.

To the best of the authors’ knowledge, this is the first empirical study to investigate the relationship between ITIs and FP from the perspective of digital options, exploring five emerging technologies and considering firm life, size, and state ownership in a sample of Chinese listed firms.

This study investigates whether the visualization of agricultural products influences consumers’ purchase intentions in the context of farmer-assisted livestreaming in China. Moreover, it explores the moderating effect of packaging functionality and the mediating effect of consumer trust.

Consumers in China from multiple social media platforms participated in this survey, which yielded 333 valid responses for analysis.

The results revealed a positive relationship between the video presentation about the agricultural production process and consumers’ purchase intention, which is mediated by consumers’ trust. Meanwhile, packaging functionality moderates the relationship between agricultural product visualization and consumers’ purchase intentions as well as the indirect effect of consumers’ trust.

This study extends the application of the stimulus-organism-response (SOR) model to the field of farmer-assisted livestreaming. By building a more detailed model, this study adds to knowledge on the influencing mechanisms of consumers’ purchase intentions in farmer-assisted livestreaming.

Objectively evaluating the fairness of the algorithm, exploring in specific scenarios combined with scenario characteristics and constructing the algorithm fairness evaluation index system in specific scenarios.

This paper selects marketing scenarios, and in accordance with the idea of “theory construction-scene feature extraction-enterprise practice,” summarizes the definition and standard of fairness, combs the application link process of marketing algorithms and establishes the fairness evaluation index system of marketing equity allocation algorithms. Taking simulated marketing data as an example, the fairness performance of marketing algorithms in some feature areas is measured, and the effectiveness of the evaluation system proposed in this paper is verified.

The study reached the following conclusions: (1) Different fairness evaluation criteria have different emphases, and may produce different results. Therefore, different fairness definitions and standards should be selected in different fields according to the characteristics of the scene. (2) The fairness of the marketing equity distribution algorithm can be measured from three aspects: marketing coverage, marketing intensity and marketing frequency. Specifically, for the fairness of coverage, two standards of equal opportunity and different misjudgment rates are selected, and the standard of group fairness is selected for intensity and frequency. (3) For different characteristic fields, different degrees of fairness restrictions should be imposed, and the interpretation of their calculation results and the means of subsequent intervention should also be different according to the marketing objectives and industry characteristics.

First of all, the fairness sensitivity of different feature fields is different, but this paper does not classify the importance of feature fields. In the future, we can build a classification table of sensitive attributes according to the importance of sensitive attributes to give different evaluation and protection priorities. Second, in this paper, only one set of marketing data simulation data is selected to measure the overall algorithm fairness, after which multiple sets of marketing campaigns can be measured and compared to reflect the long-term performance of marketing algorithm fairness. Third, this paper does not continue to explore interventions and measures to improve algorithmic fairness. Different feature fields should be subject to different degrees of fairness constraints, and therefore their subsequent interventions should be different, which needs to be continued to be explored in future research.

This paper combines the specific features of marketing scenarios and selects appropriate fairness evaluation criteria to build an index system for fairness evaluation of marketing algorithms, which provides a reference for assessing and managing the fairness of marketing algorithms.

Algorithm governance and algorithmic fairness are very important issues in the era of artificial intelligence, and the construction of the algorithmic fairness evaluation index system in marketing scenarios in this paper lays a safe foundation for the application of AI algorithms and technologies in marketing scenarios, provides tools and means of algorithm governance and empowers the promotion of safe, efficient and orderly development of algorithms.

In this paper, firstly, the standards of fairness are comprehensively sorted out, and the difference between different standards and evaluation focuses is clarified, and secondly, focusing on the marketing scenario, combined with its characteristics, key fairness evaluation links are put forward, and different standards are innovatively selected to evaluate the fairness in the process of applying marketing algorithms and to build the corresponding index system, which forms the systematic fairness evaluation tool of marketing algorithms.

Due to the limitation of experimental conditions and budget, fatigue data of mechanical components are often scarce in practical engineering, which leads to low reliability of fatigue data and reduces the accuracy of fatigue life prediction. Therefore, this study aims to expand the available fatigue data and verify its reliability, enabling the achievement of life prediction analysis at different stress levels.

First, the principle of fatigue life probability percentiles consistency and the perturbation optimization technique is used to realize the equivalent conversion of small samples fatigue life test data at different stress levels. Meanwhile, checking failure model by fitting the goodness of fit test and proposing a Monte Carlo method based on the data distribution characteristics and a numerical simulation strategy of directional sampling is used to extend equivalent data. Furthermore, the relationship between effective stress and characteristic life is analyzed using a combination of the Weibull distribution and the Stromeyer equation. An iterative sequence is established to obtain predicted life.

The TC4–DT titanium alloy is selected to assess the accuracy and reliability of the proposed method and the results show that predicted life obtained with the proposed method is within the double dispersion band, indicating high accuracy.

The purpose of this study is to provide a reference for the expansion of small sample fatigue test data, verification of data reliability and prediction of fatigue life data. In addition, the proposed method provides a theoretical basis for engineering applications.

The rapid development of e-commerce live streaming is inseparable from the important role of live-streamer. However, research exploring the impacts of live-streamers’ characteristics on customer engagement in different types of live streaming rooms remains limited. This study aims to examine the impacts of live-streamers’ characteristics (i.e. expertise, moral reputation, popularity, responsiveness) on consumer engagement in e-commerce live streaming, and verify whether there are differences in the impact of live-streamers’ characteristics on consumer experience for different types of live-streamers.

Data were gathered from 266 Chinese adults who have experience watching e-commerce live streaming. Partial least squares techniques were used to verify the model.

The results show that expertise, moral reputation, popularity, and responsiveness of live-streamers have positive impacts on consumer engagement through perceived trust and perceived pleasure. Moreover, through multi-group analysis, this study found that compared with enterprise live-streamers, the moral reputation of individual live-streamers exerts a stronger effect on consumers’ perceived trust, and the popularity of individual live-streamers exerts a stronger effect on consumers’ perceived pleasure. Compared with individual live-streamers, the responsiveness of enterprise live-streamers exerts a stronger effect on consumers’ perceived pleasure.

This study contributes to e-commerce live streaming research by categorizing live-streamers into two types: individual live-streamers and enterprise live-streamers, and verifying the specific difference in the impacts of live-streamers’ characteristics on consumer experience for the two types of live-streamers.

Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.