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Artificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR.

The present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research.

The authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management.

The present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR.

The present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.

The purpose of this study is to investigate and understand the performance of countries in individual Olympic Games, specifically focusing on the Tokyo 2020 Olympics. Employing cluster analysis and decision trees, the research aims to categorize countries based on their representation, participation and success.

This research employs a data-driven approach to comprehensively analyze and enhance understanding of countries' performances in individual Olympic Games. The methodology involves a two-stage clustering method and decision tree analysis to categorize countries and identify influential factors shaping their Olympic profiles.

The study, analyzing countries' performances in the Tokyo 2020 Olympics through cluster analysis and decision trees, identified five clusters with consistent profiles. Notably, China, Great Britain, Japan, Russian Olympic Committee and the United States formed a high-performing group, showcasing superior success, representation and participation. The analysis revealed a correlation between higher representation/participation and success in individual Olympic Games. Decision tree insights underscored the significance of population size, GDP per Capita and HALE index, indicating that countries with larger populations, better economic standing and higher health indices tended to perform better.

The study has several limitations that should be considered. Firstly, the findings are based on data exclusively from the Tokyo 2020 Olympics, which may limit the generalizability of the results to other editions.

The research offers practical implications for policymakers, governments and sports organizations seeking to enhance their country's performance in individual Olympic Games.

The research holds significant social implications by contributing insights that extend beyond the realm of sports.

The originality and value of this research lie in its holistic approach to analyzing countries' performances in individual Olympic Games, particularly using a two-stage clustering method and decision tree analysis.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Multiple jobholding (MJH) is assumed to be a growing phenomenon due to working life changes. This study presents new knowledge on the MJH career paths, from the perspectives of both employers and employees.

The qualitative interview study was focused on retail trade and restaurant and food service industries in Finland, where MJH is a quite common work arrangement compared to other European countries. The data were analyzed with the concepts of the chaos theory of careers and with an abductive thematic content analysis.

According to the results, several events and intertwined factors may lead individual careers gradually to MJH. Changing personal and family situations and leisure time needs attracted the careers towards MJH. MJH was not only a financial necessity to employees, but it also served their flexibility interests. The interviewed employers applied flexible non-standard employment arrangements mainly due to rapidly varying labor needs established in the industries. It was important for them to strengthen the non-standard core employees' sense of belonging to the work community. However, employees with work ability challenges were in risk to end up in peripheral positions at the labor market.

Previous research on multiple jobholding has not combined employers’ perspectives of MJH to employees’ experiences of career paths.

This study aims to bring together the available scattered knowledge about teaching and learning in Living Labs within higher education, and to explore their potential for supporting students’ sustainability-oriented transformative learning.

A literature review was conducted, applying a realist approach. A sample of 35 articles was analyzed qualitatively, mapping the data according to the realist constructs “context,” “intervention,” “mechanism” and “outcome” and using the constant comparison method for data analysis.

This study identified multiple characteristics of teaching and learning in sustainability-oriented Living Labs, namely, two socio-physical teaching and learning contexts, two pedagogical approaches as interventions therein, four learning processes as (potential) mechanisms and six sustainability-related learning outcomes. Two main challenges were also identified.

To the best of the authors’ knowledge, this is the first study that brings together the scattered results from previous studies into a comprehensive description of characteristics and challenges of teaching and learning in Living Labs as sustainability-oriented learning spaces in higher education. The findings can support educators in making scientifically grounded informed choices for teaching and learning in Living Labs and inform future empirical studies to examine when, how and why certain characteristics of teaching and learning in Living Labs, as identified in this study, can support sustainability-oriented transformative learning in higher education.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image.

This research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations.

The paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

The service environment is becoming increasingly turbulent, leading to calls for a systemic understanding of it as a set of dynamic service ecosystems. This paper advances this understanding by developing a typology of service ecosystem dynamics that explains the varying interplay between change and stability within the service environment through distinct behavioral patterns exhibited by service ecosystems over time.

This study builds upon a systematic literature review of service ecosystems literature and uses system dynamics as a method theory to abductively analyze extant literature and develop a typology of service ecosystem dynamics.

The paper identifies three types of service ecosystem dynamics—behavioral patterns of service ecosystems—and explains how they unfold through self-adjustment processes and changes within different systemic leverage points. The typology of service ecosystem dynamics consists of (1) reproduction (i.e. stable behavioral pattern), (2) reconfiguration (i.e. unstable behavioral pattern) and (3) transition (i.e. disrupting, shifting behavioral pattern).

The typology enables practitioners to gain a deeper understanding of their service environment by discerning the behavioral patterns exhibited by the constituent service ecosystems. This, in turn, supports them in devising more effective strategies for navigating through it.

The paper provides a precise definition of service ecosystem dynamics and shows how the identified three types of dynamics can be used as a lens to empirically examine change and stability in the service environment. It also offers a set of research directions for tackling service research challenges.

Manufacturers face various challenges and risks during their digital servitization (DS), due to the complexity caused by introducing breakthrough technologies, increasingly complex product-service solutions and new stakeholders in the business network. The process necessitates the implementation of various changes that usually happen over a long period of time. Using complexity management as a theoretical lens, this paper delves into manufacturers’ DS journeys and explores how manufacturers manage the associated complexities.

This paper investigates the DS journey of two manufacturers in a longitudinal case study from 2014 to 2021.

Three main complexity management actions during the DS journey were identified: shaping the digital service system, shaping the organization and shaping the network. Tied to different types of complexities, these actions demonstrate how manufacturers navigate their journey. The findings also reveal different complexity management approaches used at the different stages of this journey.

This paper offers a comprehensive framework for understanding complexity management in the DS journey, including the types of complexities, complexity management actions and complexity management approaches and their rationale. This paper shows that different requirements are created during emerge, consolidate and evolve stages of the DS journey. Manufacturers need a dynamic approach that considers changes in complexities and actions over time.

In the past 10 years, the scale of running events in China has increased dramatically, and the forms of running events have also become rich and diverse. Running is not only a social phenomenon but also a historical and cultural phenomenon as an organic part of human culture with its own sociological values in China. This chapter offers insight into the development of Chinese running culture and how this has emerged from ancient and modern Chinese running cultures based on Foucault's disciplinary power theory, biopower and the technologies of the self. This chapter argues that running culture in China constructs the subjectivity of the Chinese runners under the joint action of the technologies of power and the technologies of the self. The findings acknowledge how Chinese Runners present and express themselves by showing a ‘sense of presence’. Runners illustrate the implicit or explicit meaning and value of a particular way of life through running. Runners regard running as the technology of the self for self-expression and self-creation so that individuals can control their bodies and soul, thoughts, behaviours and ways of existence. Emerging technologies of power provide possibilities for the production of running culture in China, and the current policy under the technologies of power meets the needs of runners. In Chinese running culture, power was not oppressive but productive.