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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.

Organizational psychologists and human resource management (HRM) practitioners often have to select the “right fit” candidate by manually scouting data from various sources including job portals and social media. Given the constant pressure to lower the recruitment costs and the time taken to extend an offer to the right talent, the HR function has to inevitably adopt data analytics and machine learning for employee selection. This paper aims to propose the “Quality of Hire” concept for employee selection using the person-environment (P-E) fit theory and machine learning.

The authors demonstrate the aforementioned concept using a clustering algorithm, namely, partition around mediod (PAM). Based on a curated data set published by the IBM, the authors examine the dimensions of different P-E fits and determine how these dimensions can lead to selection of the “right fit” candidate by evaluating the outcome of PAM.

The authors propose a multi-level fit model rooted in the P-E theory, which can improve the quality of hire for an organization.

Theoretically, the authors contribute in the domain of quality of hire using a multi-level fit approach based on the P-E theory. Methodologically, the authors contribute in expanding the HR analytics landscape by implementing PAM algorithm in employee selection.

The proposed work is expected to present a useful case on the application of machine learning for practitioners in organizational psychology, HRM and data science.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Organizations perform better when the right candidates are hired. Through person-environment fit (P-E fit), firms can enhance recruitment and selection by ensuring appropriate fit at different levels of the construct.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Sufficient sample data are the necessary condition to ensure high reliability; however, there are relatively poor fatigue test data in the engineering, which affects fatigue life's prediction accuracy. Based on this, this research intends to analyze the fatigue data with small sample characteristics, and then realize the life assessment under different stress levels.

Firstly, the Bootstrap method and the principle of fatigue life percentile consistency are used to realize sample aggregation and information fusion. Secondly, the classical outlier detection algorithm (DBSCAN) is used to check the sample data. Then, based on the stress field intensity method, the influence of the non-uniform stress field near the notch root on the fatigue life is analyzed, and the calculation methods of the fatigue damage zone radius and the weighting function are revised. Finally, combined with Weibull distribution, a framework for assessing multiaxial low-cycle fatigue life has been developed.

The experimental data of Q355(D) material verified the model and compared it with the Yao’s stress field intensity method. The results show that the predictions of the model put forward in this research are all located within the double dispersion zone, with better prediction accuracies than the Yao’s stress field intensity method.

Aiming at the fatigue test data with small sample characteristics, this research has presented a new method of notch fatigue analysis based on the stress field intensity method, which is combined with the Weibull distribution to construct a low-cycle fatigue life analysis framework, to promote the development of multiaxial fatigue from experimental studies to practical engineering applications.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Firms can ensure that the right candidates are hired for the right jobs by identifying close person-environment fit. Use of machine learning algorithms enables HR professionals to make these decisions based on swift, efficient analysis of large volumes of relevant data.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Transformational leadership (TFL) has been identified by a number of studies as a positive force for business success. However, few studies have explicitly examined its influence on the cooperative employment relationship, such as frontline employees' voice in the workplace. Thus, this study conducts an empirical analysis of dual-level (i.e. group-focused and individual-focused) TFL and its effect on frontline employees' cooperative voice.

This empirical study is based on data from a survey of 468 full-time frontline workers from 38 hotels in China.

The empirical results revealed that both individual-level and group-level TFL are significantly associated with frontline employees' cooperative voice. Person–Organization value congruence and Person–Supervisor value congruence both act as mediators in the linkage between group-level TFL and employees' cooperative voice behaviors. The partial mediating role of Demand–Ability congruence on the relationship between individual-level TFL and cooperative voice is also established.

First, this study investigates the dual-level effects of TFL on the cooperative voice behavior of frontline employees. Second, this study explores whether three dimensions of value congruence mediate the influence of TFL on the voice behaviors of employees.

This paper aims to investigate the effect of business incubation metrics based on an adaptation of the lean start-up (LS) framework on start-up survival after incubation. This study also analyzes the obstacles in implementing the LS framework as incubation metrics.

This study uses mixed methods. Quantitative research using multiple linear regression was applied to the data of 30 start-ups incubated at Bandung Techno Park for the 2014–2017 period and survival tracking data after the incubation. A qualitative approach to complete the explanatory work was conducted through in-depth interviews with 12 respondents, including start-up graduates from the incubation program, program managers and mentors.

This study confirms that several LS incubation metrics significantly affect start-up sustainability after incubation. In addition, this study also explains several problems in applying the LS discipline that needs attention to increase incubation success.

Research was conducted only at one technology business incubator (TBI) model that focuses on digital start-ups in the emerging ecosystem. Research results can be biased in different situations and ecosystems.

The explanation of the relationship of LS-based incubation metrics to the survival of start-ups, as well as the challenges of their implementation, can be a reference for TBI management to consider and prioritize intervention strategies, thereby improving TBI’s business processes and increasing the success rate of incubated start-ups.

The creation of university start-ups and spin-offs has become a key performance indicator mandatory for technology universities in Indonesia. The existence of TBI institutions in universities as channels of technology commercialization is essential. The incubator’s success in creating a new technology-based company will have a significant social impact on the surrounding environment.

Although the LS method is popular in start-up communities and among practitioners, it is rarely used in the incubation process at universities. These results can be considered for university TBIs to explore LS as an incubation management tool to increase the success rate of incubated start-ups.

Although the Net Promoter Score (NPS) index is simple, NPS has weaknesses that make NPS's interpretation misleading. The main criticism is that identical index values can correspond to different levels of customer loyalty. This makes difficult to determine whether the company is improving/deteriorating in two different years. The authors describe the application of statistical tools to establish whether identical values may/may not be considered similar under statistical hypotheses.

Equal NPSs with a “similar” component composition should have a two-way table satisfying marginal homogeneity hypothesis. The authors compare the marginals using a cumulative marginal logit model that assumes a proportional odds structure: the model has the same effect for each logit. Marginal homogeneity corresponds to null effect. If the marginal homogeneity hypothesis is rejected, the cumulative odds ratio becomes a tool for measuring the proportionality between the odds.

The authors propose an algorithm that helps managers in their decision-making process. The authors' methodology provides a statistical tool to recognize customer base compositions. The authors suggest a statistical test of the marginal distribution homogeneity of the table representing the index compositions at two times. Through the calculation of cumulative odds ratios, the authors discriminate against the hypothesis of equality of the NPS.

The authors' contribution provides a statistical alternative that can be easily implemented by business operators to fill the known shortcomings of the index in the customer satisfaction's context. This paper confirms that although a single number summarizes and communicates a complex situation very quickly, the number is ambiguous and unreliable if not accompanied by other tools.

The purpose of this research is to examine the relationship between coaching leadership and employees' taking charge while incorporating the mediating role of work meaningfulness and the moderating role of challenge-hindrance stressor.

A total of 355 pairs of effective samples were collected through a two-stage supervisor-subordinate paired survey. Four hypotheses were tested using hierarchal regression analysis and bootstrapping method.

The findings show that coaching leadership is positively related to taking charge, and work meaningfulness positively mediates the coaching leadership-taking charge relationship; high challenge stressors and high hindrance stressors weaken the positive effect of coaching leadership on work meaningfulness respectively; challenge stressors and hindrance stressors further moderate the indirect relationship of coaching leadership and taking charge through work meaningfulness.

This study provides a new perspective for organizations to activate employees' taking charge, thereby enriching the antecedents of taking charge. By incorporating challenge-hindrance stressor framework, this study also provides answers to when coaching leadership will be less effective.

Our proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.

Within the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.

The K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.

Employee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.

This study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.