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The purpose of this paper is to examine feature fatigue with products and how to deal with it.

The paper looks at feature fatigue and how products perform on the market. It then suggests the right and wrong ways of defeating fatigue.

The paper reveals the problem of too many features in a product leading to fatigue. However, it suggests that, with increasingly demanding consumers and ever‐shortening product life cycles, firms should attempt to defeat feature fatigue not by reducing features, but by improving product designs that reduce the fatigue. Otherwise their products can disappoint consumers and perform poorly on the market, opening an opportunity for competitors.

This paper presents useful information on ways to deal with customer fatigue with product features.

The purpose of this paper is to focus on understanding how social influence and personality of individuals differentiate between users’ social network fatigue and discontinuance behavior. Furthermore, the most common discontinuance behavior among users was investigated.

The research model was tested with the data from 163 Instagram users based on online and offline surveys. The partial least squares method was used to test the proposed hypotheses of this study.

The results indicate that social influence affects users’ discontinuance behavior and social network fatigue. Social network fatigue is greater in users with higher reported social influence compared to those with a lower one. Moreover, in response to social network fatigue, users prefer to keep their activities under control instead of switching to alternative social network sites (SNSs) or a short break in social network activities.

By achieving a better understanding of users’ feeling and behaviors, social network providers may codify their strategies more efficiently.

The study is novel in exploring users’ SNS fatigue and their discontinuance behavior by integrating social influence and personality. The authors defined a new concept of effect of social influence on social network fatigue. Additionally, the authors examined which discontinuance behaviors in individuals were more prevalent.

This paper aims to propose a novel method to predict and alleviate feature fatigue. Many products now are loaded with an extensive number of features. Adding more features to one product generally makes the product more attractive on the one hand but, on the other hand, may result in increasing difficulty to use the product. This phenomenon is called “feature fatigue”, which will lead to dissatisfaction and negative word-of-mouth (WOM). Feature fatigue will damage the brand’s long-term profit, and ultimately decrease the manufacturer’s customer equity. Thus, a problem of balancing the benefit of increasing “attractiveness” with the cost of decreasing “usability” exists.

A novel method based on the Bass model is proposed to predict and alleviate feature fatigue. Product capability, usability and WOM effects are integrated into the Bass model to predict the impacts of adding features on customer equity in product development, thus helping designers alleviate feature fatigue. A case study of mobile phone development based on survey data is presented to illustrate and validate the proposed method.

The results of the case study demonstrate that adding more features indeed increases initial sales; however, adding too many features ultimately decreases customer equity due to usability problems. There is an optimal feature combination a product should include to balance product capability with usability. The proposed method makes a trade-off between initial sales and long-term profits to maximize customer equity.

The proposed method can help designers predict the impacts of adding features on customer equity in the early stages of product development. It can provide decision supports for designers to decide what features should be added to maximize customer equity, thus alleviating feature fatigue.

Reports on the development of the Super CMV mainshaft for the Trent class jet engine from Rolls‐Royce. Describes in depth how the new mainshaft was designed to outperform the earlier generations. In comparison with the previous generation, the new mainshaft, through the use of improved materials, is able to cope with approximately twice the amount of torque transmitted to the fan, has a shaft weight per unit of transmitted torque ratio of 25 per cent less, a diameter that is similar in size, and yet was still able to be manufactured at a similar cost. Also reports on the increase in fatigue lives for the shaft oil holes and splines that was achieved through design improvements.

Despite the various benefits of social networking services (SNSs), users have begun to experience fatigue in recent years, as evidenced by a decline in active user numbers. This relatively new phenomenon has only recently received significant managerial and academic attention. The antecedents of SNS fatigue are still unclear in the literature. The purpose of this paper is to identify the key factors causing SNS fatigue, based on a socio-technical approach.

The authors empirically tested this research model with 424 SNS users via an online survey. Structural equation modeling with partial least squares was used to analyze the data.

The results showed that the social factors of social comparison, social interaction overload, social surveillance and social information overload, and the technical factor of system complexity are significantly related to SNS fatigue.

This research benefits SNS providers by allowing them to better understand how to effectively design social networking platforms to retain and attract more users. It also benefits users by providing guidance on how to actively manage their own behavior to avoid potential negative outcomes induced by SNS usage.

This study focuses on the “dark side” of SNS from the perspective of fatigue, extending existing research on technostress. It also extends the applicability of the socio-technical approach to the phenomenon of SNS fatigue. Moreover, it enriches SNS fatigue research by addressing the importance of both social and technical factors in causing SNS fatigue.

This paper investigates the moderating role of multitasking computer self-efficacy on the relationship between social media affordances and social media overload as well as its moderation between social media overload and social media fatigue.

The authors hypothesize that social media affordances will have a positive impact on social media overload (i.e. information and communication overload). They also hypothesize that social media overload will affect social media fatigue. In addition, they hypothesize that multitasking computer self-efficacy will attenuate the effect of social media affordances on both information overload and communication overload. Similarly, they also hypothesize that multitasking computer self-efficacy will attenuate the effects of both information overload and communication overload on fatigue. The authors test this model by collecting two-wave data from 220 professionals using PLS techniques.

Social media affordances have significant impacts on information overload, but not on communication overload. In turn, information overload and communication overload significantly affect social media fatigue. Multitasking computer self-efficacy was found to attenuate the effect of social media affordances on both information overload and communication overload. Furthermore, the study results suggest that multitasking computer self-efficacy attenuates the effect of information overload and reinforces the effect of communication overload on social media fatigue.

Most prior literature focused on students rather than on professionals. There is a lack of research that investigates how the affordances of social media relate to social media overload and fatigue. Furthermore, research that investigates mitigating mechanisms of social media fatigue has been rare. This paper fills these important research gaps.

This study focuses on perceived overload from environmental stimuli and individual psychology and behavioral interactions. It constructs a theoretical model with overload as the key stressor based on the stressor-strain-outcome (SSO) model. The authors argue that system feature overload (SFO), information overload, and social overload lead to two psychological strains: fear of missing out (FoMO) and fatigue among users of short video platforms, affecting their discontinuous usage intentions.

To test the hypotheses, the authors conducted a questionnaire survey on 412 users' short video platform usage and empirically tested the constructed model using the research tool SmartPLS 3.3.2.

The results of data analysis showed that most of the hypotheses were supported. Specifically, system feature overload, information overload and social overload all positively affected FoMO. However, SFO and information overload significantly affected fatigue. There was no significant relationship between social overload and fatigue. In addition, both FoMO and fatigue negatively influenced users' discontinuous usage intentions.

The current research on user behavior in information systems tends to focus on the influence in the positive direction and less on the negative direction. The research on discontinuous usage intention (DUI) is a very new research topic. This research studies the influencing factors of users' discontinuous behavior from the perspective of perceptual overload. It provides a unique view for future short video platform user behavior research, with significant theoretical contributions and essential practice for short video platform operators to improve services.

This paper aims to present the novel stacked machine learning approach (SMLA) to estimate low-cycle fatigue (LCF) life of SAC305 solder across structural parts. Using the finite element simulation (FEM) and continuous damage mechanics (CDM) model, a fatigue life database is built. The stacked machine learning (ML) model's iterative optimization during training enables precise fatigue predictions (2.41% root mean square error [RMSE], R² = 0.975) for diverse structural components. Outliers are found in regression analysis, indicating potential overestimation for thickness transition specimens with extended lifetimes and underestimation for open-hole specimens. Correlations between fatigue life, stress factors, nominal stress and temperature are unveiled, enriching comprehension of LCF, thus enhancing solder behavior predictions.

This paper introduces stacked ML as a novel approach for estimating LCF life of SAC305 solder in various structural parts. It builds a fatigue life database using FEM and CDM model. The stacked ML model iteratively optimizes its structure, yielding accurate fatigue predictions (2.41% RMSE, R² = 0.975). Outliers are observed: overestimation for thickness transition specimens and underestimation for open-hole ones. Correlations between fatigue life, stress factors, nominal stress and temperature enhance predictions, deepening understanding of solder behavior.

The findings of this paper highlight the successful application of the SMLA in accurately estimating the LCF life of SAC305 solder across diverse structural components. The stacked ML model, trained iteratively, demonstrates its effectiveness by producing precise fatigue lifetime predictions with a RMSE of 2.41% and an “R²” value of 0.975. The study also identifies distinct outlier behaviors associated with different structural parts: overestimations for thickness transition specimens with extended fatigue lifetimes and underestimations for open-hole specimens. The research further establishes correlations between fatigue life, stress concentration factors, nominal stress and temperature, enriching the understanding of solder behavior prediction.

The authors confirm the originality of this paper.

Grounded on the cognition–affect–conation (C–A–C) framework, this study aims to explore how perceived information overload affects the information avoidance intention of social media users through fatigue, frustration and dissatisfaction.

A quantitative research design is adopted. The data collected from 254 respondents in China are analyzed via structural equation modeling (SEM).

Perceived information overload directly affects fatigue, frustration and dissatisfaction among social media users, thereby affecting their information avoidance intention. In addition, frustration significantly affects social media fatigue and dissatisfaction. Consequently, social media fatigue influences dissatisfaction among users.

The literature review indicates that social media overload and fatigue yield negative behavioral outcomes, including discontinuance. However, rather than completely abstaining or escaping, social media users adopt moderate strategies, including information avoidance, to cope with overload and fatigue owing to their high dependence on social media. Unfortunately, merely few studies are available on the information avoidance behavior of social media users. Focusing on this line of research, the current study develops a model to investigate the antecedents of information avoidance in social media.

The purpose of this paper is to present the application of Hilbert–Huang transform (HHT) for fatigue damage feature characterisation in the time–frequency domain based on strain signals obtained from the automotive coil springs.

HHT was employed to detect the temporary changes in frequency characteristics of the vibration response of the signals. The extraction successfully reduced the length of the original signal to 40 per cent, whereas the fatigue damage was retained. The analysis process for this work is divided into three stages: signal characterisation with the application of fatigue data editing (FDE) for fatigue life assessment, empirical mode decomposition with Hilbert transform, an energy–time–frequency distribution analysis of each intrinsic mode function (IMF).

The edited signal had a time length of 72.5 s, which was 40 per cent lower than the original signal. Both signals were retained statistically with close mean, root-mean-square and kurtosis value. FDE improved the fatigue life, and the extraction did not affect the content and behaviour of the original signal because the editing technique only removed the minimal fatigue damage potential. HHT helped to remove unnecessary noise in the recorded signals. EMD produced sets of IMFs that indicated the differences between the original signal and mean of the signal to produce new components. The low-frequency energy was expected to cause large damage, whereas the high-frequency energy will cause small damage.

HHT and EMD can be used in the strain data signal analysis of the automotive component of a suspension system. This is to improve the fatigue life, where the extraction did not affect the content and behaviour of the original signal because the editing technique only removed the minimal fatigue damage potential.