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Anxiety, depression and impaired health-related quality of life (HRQoL) are commonly reported in patients with multiple sclerosis (MS) and are of great interest for therapeutic approaches. Based on regional differences a quantitative assessment of these factors in comparison to the general population, and the consideration of demographic cofactors, would be useful when designing specific interventions. We adopted such an approach in a German cohort of MS patients. Anxiety, depression (HADS) and HRQoL (SF-36) were measured in 49 consecutive outpatients with MS and compared to age- and gender-adjusted control groups (n=1330 for HADS; n=5087 for SF-36) extracted from German National Health Surveys. Patients with MS showed significantly increased levels of anxiety and depression as well as decreased HRQoL with the exception of mental health; the effect sizes ranged from 0.39 (depression) to 1.06 (physical functioning). As could be expected, MS patients with relapsing-remitting clinical course had better physical functioning than patients with secondary progressive MS. There were strong relations between anxiety and depression (r=0.54; P<0.01), and between neurological impairment (EDSS) and physical functioning (r=-0.80; P<0.001) as well as depression (r=0.48; P<0.05). This investigation of MS patients confirms the prevalence and impact of anxiety, depression and most of the HRQoL dimensions in MS patients and provides evidence for the usefulness of a quantitative comparison to a region-specific general population as a starting point for therapeutic approaches.

The article discusses the current relevance of artificial intelligence (AI) in research and how AI improves various research methods. This article focuses on the practical case study of systematic literature reviews (SLRs) to provide a guideline for employing AI in the process.

Researchers no longer require technical skills to use AI in their research. The recent discussion about using Chat Generative Pre-trained Transformer (GPT), a chatbot by OpenAI, has reached the academic world and fueled heated debates about the future of academic research. Nevertheless, as the saying goes, AI will not replace our job; a human being using AI will. This editorial aims to provide an overview of the current state of using AI in research, highlighting recent trends and developments in the field.

The main result is guidelines for the use of AI in the scientific research process. The guidelines were developed for the literature review case but the authors believe the instructions provided can be adjusted to many fields of research, including but not limited to quantitative research, data qualification, research on unstructured data, qualitative data and even on many support functions and repetitive tasks.

AI already has the potential to make researchers’ work faster, more reliable and more convenient. The authors highlight the advantages and limitations of AI in the current time, which should be present in any research utilizing AI. Advantages include objectivity and repeatability in research processes that currently are subject to human error. The most substantial disadvantages lie in the architecture of current general-purpose models, which understanding is essential for using them in research. The authors will describe the most critical shortcomings without going into technical detail and suggest how to work with the shortcomings daily.

A growing body of literature shows how intragenerational occupational mobility affects economic dynamics and social stratification. In this article the authors aim to carry out a structured review of this literature, outlining a systemic overview for more comprehensive research and public policies.

The authors use methods from structured literature reviews and network science to reveal the segmented research landscape of occupational mobility literature. The authors made an in-depth analysis of the most important papers to summarize the main contributions of the literature and identify research gaps.

The authors reveal a segmented research landscape around three communities: (1) human capital theory, (2) social stratification theory and (3) migration studies. Human capital research uses microfounded mathematical modeling to understand the relationship between skills and mobility. Nevertheless, it cannot explain social segregation and generally does not focus on the importance of local labor demand. Social stratification research can explain the social and institutional barriers to occupational mobility. Migration research studies the relationship between migration, labor demand and social mobility.

This paper is the first literature review that uses network analysis to perform a systematic review of the intragenerational occupational mobility literature. Moreover, this review identifies opportunities for mutual learning and research gaps in the research landscape.

This study aims to investigate different types of platform providers (PPs) to gain a deeper understanding of the characteristics and underlying logic of this group within collaborative consumption (CC). As CC occurs with three groups of actors (PP, peer service provider and customer) and is predominantly viewed from the customer perspective, this study offers insights from the under-researched PP perspective.

This research applies a multiple case study approach and analyzes descriptively and thematically 92 cases of CC PPs gathered through the Crunchbase database.

The authors derive four archetypes of CC PPs, namely, the hedonist, functionalist, environmentalist and connector, that differ in their offered values, dominating motives and activities across industries.

The authors conceptualize CC by clearly describing the four archetypes and their characteristics. However, further research would benefit from including databases other than Crunchbase.

PPs need to understand their value offerings and customer preferences to develop convincing value propositions and offer engaging activities. PPs would benefit from a more active social media presence to build strong relations with customers and peer service providers to effectively communicate their values.

The paper is pioneering as it encompasses the perspective of CC PPs and operationalizes the concept of CC. The authors address the lack of research on CC by conducting an extensive case study.

Recent years have seen a meteoric rise in the study of narcissism in entrepreneurship, although little consolidation has occurred in this area. The purpose of this paper is the development of an integrative framework to harmonise the academic discussion and serve as a structured foundation for future research.

The authors conducted an artificial intelligence-aided, structured literature review focused on content analysis of concepts and contexts to map out current findings and research gaps in startup narcissism research.

According to the findings of this study, narcissistic tendencies have the potential to positively influence startup success early on in an entrepreneur's journey, but after a certain point in the process, the influence of narcissism on success becomes predominantly negative.

The research field is currently not very harmonised regarding research measures, research subjects and key research terms. Further research must use a standardised approach to add value to the research body.

Narcissism is a two-sided sword for founders. In the early stages of a company, many of the founder’s tasks can benefit from narcissistic tendencies. In the later stages of a company, that might shift to overwhelmingly negative effects of narcissism.

Methodically, this study is the first one to establish an artificial intelligence component to add value to the results of a review paper to the best of the authors’ knowledge. The results of this study provide a clear framework of entrepreneurial intention, entrepreneurial activity and entrepreneurial performance to give researchers the opportunity of a more differentiated way of organising work.

The technological revolution in the service sector is radically changing the ways in which and with whom consumers co-create value. This conceptual paper considers social robots in elderly care services and outlines ways in which their human-like affect and cognition influence users’ social perceptions and anticipations of robots’ value co-creation or co-destruction potential. A future research agenda offers relevant, conceptually robust directions for stimulating the advancement of knowledge and understanding in this nascent field.

Drawing from service, robotics and social cognition research, this paper develops a conceptual understanding of the value co-creation/destruction potential of social robots in services.

Three theoretical propositions construct an iterative framework of users’ evaluations of social robots in services. First, social robots offer users value propositions leveraging affective and cognitive resources. Second, users’ personal values become salient through interactions with social robots’ affective and cognitive resources. Third, users evaluate social robots’ value co-creation/destruction potential according to social cognition dimensions.

Social robots in services are an emerging topic in service research and hold promising implications for organizations and users. This relevant, conceptually robust framework advances scholarly understanding of their opportunities and pitfalls for realizing value. This study also identifies guidelines for service managers for designing and introducing social robots into complex service environments.

The purpose of this paper is to clarify whether the dark side of personality adds information beyond the bright side when predicting career success.

In total, 287 participants (150♀, M_age=37.74 and SD_age=10.38) completed questionnaires on the Dark Triad (narcissism, Machiavellianism and psychopathy) and the Big Five (emotional stability, extraversion, openness, agreeableness and conscientiousness). They also provided information on their objective (salary and leadership position) and subjective (job satisfaction and satisfaction with income) career success. Regression analyses were used to estimate the Dark Triad’s incremental predictive value.

The results show that the Dark Triad only provides incremental information beyond the Big Five when predicting salary (ΔR²=0.02*) and leadership position (ΔR²=0.04*). In contrast, the Dark Triad does not explain unique variance when predicting job satisfaction or satisfaction with income.

The exclusive use of self-rated success criteria may increase the risk of same-source biases. Thus, future studies should include ratings derived from multiple perspectives.

Considering the Dark Triad in employee selection and development seems particularly promising in the context of competitive behaviour.

The results are discussed in light of the socioanalytic theory. This may help to better understand behaviour in organisational contexts.

This study is the first that simultaneously investigates all three traits of the Dark Triad and the Big Five in combination with objective and subjective career success. In addition, it extends previous findings by answering the question of whether the Dark Triad offers incremental or redundant information to the Big Five when predicting success.

The purpose of this paper is to identify actions and guidelines for enabling and fostering the Industry 4.0 adoption, as well as to understand the role of three ecosystem actors in these actions (i.e. companies, educational organizations and regional policy makers).

52 semi-structured expert interviews in the Tyrol-Veneto cross-border macro-region were carried out and interpreted using the innovation ecosystem concept. In particular, drawing from this latter, six ecosystem building blocks were identified and used to analyze the interviews' content.

The findings allow not only to build a comprehensive framework for action to support Industry 4.0 adoption, but also to confirm the importance of exploring Industry 4.0 through the lens of the ecosystem concept. Indeed, the authors show that R&D activities should be complemented with interorganizational actions, such as training and networking, and that all ecosystem actors should be involved in the Industry 4.0 adoption.

This is among the few studies that adopt the innovation ecosystem perspective to explore best practices for Industry 4.0 adoption, thus overcoming the weakness of existing papers based on a firm-level perspective. It also complements previous ecosystem-based research on Industry 4.0 by exploring the technology adoption side, rather than the technology provision one, and by considering the adoption of a wide set of technologies.

The purpose of this paper is to propose and evaluate the method for grasping a defined set of objects in an unstructured environment. To this end, the authors propose the method of integrating convolutional neural network (CNN)-based object detection and the category-free grasping method. The considered scenario is related to mobile manipulating platforms that move freely between workstations and manipulate defined objects. In this application, the robot is not positioned with respect to the table and manipulated objects. The robot detects objects in the environment and uses grasping methods to determine the reference pose of the gripper.

The authors implemented the whole pipeline which includes object detection, grasp planning and motion execution on the real robot. The selected grasping method uses raw depth images to find the configuration of the gripper. The authors compared the proposed approach with a representative grasping method that uses a 3D point cloud as an input to determine the grasp for the robotic arm equipped with a two-fingered gripper. To measure and compare the efficiency of these methods, the authors measured the success rate in various scenarios. Additionally, they evaluated the accuracy of object detection and pose estimation modules.

The performed experiments revealed that the CNN-based object detection and the category-free grasping methods can be integrated to obtain the system which allows grasping defined objects in the unstructured environment. The authors also identified the specific limitations of neural-based and point cloud-based methods. They show how the determined properties influence the performance of the whole system.

The authors identified the limitations of the proposed methods and the improvements are envisioned as part of future research.

The evaluation of the grasping and object detection methods on the mobile manipulating robot may be useful for all researchers working on the autonomy of similar platforms in various applications.

The proposed method increases the autonomy of robots in applications in the small industry which is related to repetitive tasks in a noisy and potentially risky environment. This allows reducing the human workload in these types of environments.

The main contribution of this research is the integration of the state-of-the-art methods for grasping objects with object detection methods and evaluation of the whole system on the industrial robot. Moreover, the properties of each subsystem are identified and measured.

This paper proposes adequate strategies that companies, public administrators and organisations in the education industry can undertake to successfully face the challenges of digital transformation in a regional innovation system. This research considers stakeholders that operate in the Tyrol–Veneto macroregion (the Tyrol, South Tyrol and Veneto areas), a significant case of moderately innovative European macroregion.

This study undertakes explorative research based on a qualitative method. It adopts a place-based multi-stakeholder approach to emphasise the role of three categories of stakeholders (companies, educational system and regional governments) in facing digital changes. More precisely, interviews with 60 stakeholders from the Tyrol–Veneto macroregion were conducted and examined via both text mining analysis and content analysis. First, correspondence factor analysis was performed using IRaMuTeQ software to identify homogeneous subsets of concepts (pillars–i.e., macroareas of strategic actions). Second, two coding phases were implemented using NVivo software to detect strategic fields of action and specific strategic actions undertaken to address the challenges of digital transformation.

The results highlight that digital transformation is a pervasive challenge of regional innovative system that requires a multifaceted set of strategic actions falling into three main pillars. The first pillar, named “culture and skills”, includes three strategic fields of action as follows: digital education, talents and digital culture. The second pillar, named “infrastructures and technologies”, points out the need of information, interaction and artificial intelligence as key strategic fields of action. The third pillar, named “ecosystems”, highlights the importance of investing in medium- to long-term visions, partnerships and life quality. In brief, this study shows that standalone interventions are insufficient to tackle digital transformation from a systemic perspective. Moreover, this study outlines the potential contribution of each category of stakeholder to foster the digitalisation of the Tyrol–Veneto macroregion.

This study highlights the importance of developing digital culture and skills before investing in digital infrastructure and technology in a moderately innovative macroregion. Companies should alter their vision before reconfiguring their business models, invest in smart working and establish contacts with start-ups. In addition, this study recommends that public administration should mainly invest in digital education and partnerships, while, in terms of education and training organisations, it suggests providing digital skills to several cohorts of both students and workers. Policy implications call for the creation of new occasions of cooperation among stakeholders by fostering “table talks” as strategic and policy actions and by making more financial resources available to encourage the digital transformation processes.

The results of this study may be adapted to the characteristics of other regional innovative systems and used as a reference point in terms of the improvement of business, market and local development.