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This paper provides a systematization of the existing body of literature on both employee participation goals and the intervention formats in the context of organizational change. Furthermore, degrees of employee involvement that the intervention formats address are identified and related to the goals of employee participation. On this basis, determinants of employee involvement and participation in the context of digital transformation are unveiled.

Based on a systematic literature review the authors structure and relate employee participation goals and formats. Through a workshop with expert practitioners, the authors transfer and enhance these theoretical findings in the context of digital transformation. Experts rated the three most important goals and identified accompanying success factors, barriers and effects.

The results show that it is not necessarily the degree of involvement but a context-specific selection of measures, the quality of their implementation as well as the actual uptake of suggestions and activities developed by employees that contribute to employees accepting and participating in goal-directed transformations. Moreover, employees must have sufficient information and time for their participation in transformation processes.

This paper is based on a transformative approach, combining literature analysis to identify formats and goals of employee participation with experiential knowledge of digital transformation practitioners. In addition to relating intervention formats to goals pursued in organizational change processes, empirical and experiential perspectives are used to identify three very relevant goals and respective determinants in digital transformation processes.

Transaction cost economics sees a broad spectrum of governance structures spanned by two types of economic adaptation: autonomous and cooperative. Stakeholder theorists have drawn much inspiration from transaction cost economics but have not paid explicit attention to the centrality of the idea of adaptation in this literature. This study aims to address this gap.

The authors develop a novel conceptual framework applying the distinction between the two types of economic adaptation to stakeholder theory.

The authors argue that the idea of cooperative adaptation is particularly useful for describing the firm’s collaboration with primary stakeholders in the joint value creation process. In contrast, autonomous adaptation is more relevant for firms interacting with secondary stakeholders who are not directly engaged in joint value creation and may not have formal contractual relationships with the firm. Accordingly, cooperative adaptation can be seen as vital for resolving team production problems affecting joint value creation, whereas autonomous adaptation addresses how the firm maintains legitimacy within the larger stakeholder environment.

Similar to its significance for transaction cost economics, the distinction between the two types of adaptation equips stakeholder theory with a new systematic understanding of a potentially broad spectrum of firm–stakeholder collaboration forms.

It is important to understand why some workers prolong their working life even though they are entitled to statutory pension benefits. This paper aims to investigate whether senior workers are motivated by external factors such as pay and social expectations (extrinsic motivation) or are primarily motivated by internal factors such as job satisfaction (intrinsic motivation). This is a central question for policymakers and social partners when it comes to the design of public pension systems and work organisation.

This study uses a combined longitudinal administrative data and an own-designed postal survey to ask individuals aged 65–76 years to describe their work motivation. Based on the answers, this study constructs an index of autonomous motivation (AM) where a value of zero implies only extrinsic motivation and a value of one implies only intrinsic motivation. The values between zero and one thus imply various grades of AM, where higher values signal motivation that is more autonomous and hence a higher degree of intrinsic work motivation.

The results of the statistical analysis show that the extent of intrinsic motivation is higher among senior workers who retired aged 65 years or older compared to those who retired at 65 years or younger. In addition, this study found that the degree of intrinsic work motivation among senior workers decreases when they face economic and financial constraints. It also found that intrinsic motivation is more prevalent among high-skilled workers.

This study shows that individuals who continue to work after 65 are mostly motivated by the satisfaction they derive from their job. Job satisfaction is strongly related to skill level, job quality, job content and job autonomy. Results indicate that job quality and commitment to work are essential elements for motivating seniors to postpone retirement.

This study contributes to this literature by applying a multidisciplinary approach from organisational psychology and labour economics that considers the potential importance of intrinsic motivation to work after standard retirement age. The authors think that this approach enhances the understanding of the mechanisms behind the lengthening of working life. Finally, this study suggests a simple, but efficient way of empirically measuring the extent of intrinsic motivation among workers.

Research has demonstrated that varying degrees and combinations of individual traits, behaviors and characteristics influence the emergence of leaders in teams. While existing models have shown that such variables affect leader choice independently and interactively, the overall findings suggest that there is still a lack of research on a potentially significant factor, the impact of personal reputation on leader selection in autonomous work teams (AWTs), an increasingly ubiquitous team practice in organizations. This preliminary review thus aims to offer a general overview of personal reputation and its effects on leader emergence in the context of AWTs.

By surveying extant literature on team leader emergence, this review has identified that the potential impact of personal reputation on leader selection in teams is significant yet largely ignored.

Models of leader emergence in teams should incorporate the realistic assumption that teams formed in organizations are often comprised of individuals who have information on others either directly or indirectly. Personal reputation based on an individual’s history thus moderates how one’s behaviors or traits exhibited become contextualized in the overall assessment of leader desirability and selection in teams.

Based on a review of existing research on leadership emergence and personal reputation, this work contends that the external validity and predictive value of leader emergence models would greatly benefit from the inclusion of employee reputations as a moderating variable in the future assessment of the leader emergence process in AWTs.

By designing models that explain the potential effects of personal reputations on leader emergence in self-managed teams, team members and managers can be better elucidated and ultimately improve their understanding of the process of the evaluation and selection of team leaders.

Despite the prolific research on leader emergence, there is still a paucity of studies examining personal reputational effects on leader selection, especially in the context of AWTs. This work is the first review piece calling for the inclusion of personal reputation, a substantive factor overlooked and excluded in previous models, to enhance the current understanding of leader emergence in AWTs.

Academics and practitioners have long acknowledged the potential of multiagent systems (MAS) to automate and autonomize decision-making in logistics and supply chain networks. Despite the manifold promises of MAS, industry adoption is lagging behind, and the exact benefits of these systems remain unclear. This study aims to fill this knowledge gap by analyzing 11 specific MAS use cases, highlighting their benefits, clarifying how they can help enhance logistics network resilience and identifying existing barriers.

A three-stage Delphi study was conducted with 18 industry experts. In the first round, these experts identified 11 use cases of MAS and their potential benefits, as well as any barriers that could hinder their adoption. In the second round, they assessed the identified use cases with regard to their potential to enhance logistics network resilience and improve organizational productivity. Furthermore, they estimated the complexity of MAS implementation. In the third round, the experts reassessed their evaluations in light of the evaluations of the other study participants.

This study proposes 11 specific MAS use cases and illustrates their potential for increasing logistics network resilience and enhancing organizational performance due to autonomous decision-making in informational processes. Furthermore, this study discusses important barriers for MAS, such as lack of standardization, insufficient technological maturity, soaring costs, complex change management and a lack of existing use cases. From a theoretical perspective, it is shown how MAS can contribute to resilience research in supply chain management.

The identification and assessment of diverse MAS use cases informs managers about the potential of this technology and the barriers that need to be overcome.

This study fills a gap in the literature by providing a thorough and up-to-date assessment of the potential of MAS for logistics and supply chain management. To the best of the authors’ knowledge, this is the first study to investigate the relevance of MAS for logistics network resilience using the Delphi method.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

This paper aims to understand what drives people – their motivations, autonomous learning attitudes and learning interests – to volunteer as mentors for a program that helps families to ideate technological solutions to community problems.

A three-phase method was used to build volunteer mentor profiles; elicit topics of interest and establish relationships between those. The mentor profiles were based on self-assessments of motivation, attitude toward lifelong learning and self-regulated learning strategies. The topics of interest were elicited through content analysis of answers to reflection questions. Statistical methods were applied to analyze the relationship between the interests and the mentor profiles.

Bottom-up clustering led to the identification of three mentor groups (G1 “low”; G2 “high” and G3 “medium”) based on pre-survey data. While content analysis led to identifying topics of interest: communication skills; learning AI; mentoring; prototype development; problem-solving skills; working with families. Analyzing relationships between mentor profile and the topics of interest, the group G3 “medium,” with strong intrinsic motivation, showed significantly more interest in working with families. The group with the overall highest scores (G2 “high”) evidenced also substantial interest in learning about AI, but with high variability between members of the group.

The study established different types of learning interests of volunteer mentors and related them to the mentor profiles based on motivation, self-regulated learning strategies and attitudes toward lifelong learning. Such knowledge can help organizations shape the volunteering experience to provide more value to volunteers. Furthermore, the reflection questions can be used by volunteers as an instrument for reflection and by organizations to elicit the learning interests of volunteers.

This paper studies social inequality in the vital field of employment in Spain during the crisis period 2009-2014.

Factor analysis is used to build a synthetic index of employment exclusion. The starting information matrix collects data from a wide set of employment variables for all 17 Spanish autonomous communities and the autonomous cities of Ceuta and Melilla. Based on this information, four factors are extracted which explain employment exclusion in different situations of vulnerability, such as unemployment, temporality, poverty or low pay.

In the territorial ranking, Madrid, Basque Country, Aragon and Catalonia show the lowest risk of employment exclusion, whereas Ceuta, Andalusia, Extremadura and Canary Islands show the highest ones.

The main value of this research is that it confirms the need for coordination of public policies in order to foster social and territorial cohesion in Spain.

This paper presents a survey of research into interactive robotic systems for the purpose of identifying the state of the art capabilities as well as the extant gaps in this emerging field. Communication is multimodal. Multimodality is a representation of many modes chosen from rhetorical aspects for its communication potentials. The author seeks to define the available automation capabilities in communication using multimodalities that will support a proposed Interactive Robot System (IRS) as an AI mounted robotic platform to advance the speed and quality of military operational and tactical decision making.

This review will begin by presenting key developments in the robotic interaction field with the objective of identifying essential technological developments that set conditions for robotic platforms to function autonomously. After surveying the key aspects in Human Robot Interaction (HRI), Unmanned Autonomous System (UAS), visualization, Virtual Environment (VE) and prediction, the paper then proceeds to describe the gaps in the application areas that will require extension and integration to enable the prototyping of the IRS. A brief examination of other work in HRI-related fields concludes with a recapitulation of the IRS challenge that will set conditions for future success.

Using insights from a balanced cross section of sources from the government, academic, and commercial entities that contribute to HRI a multimodal IRS in military communication is introduced. Multimodal IRS (MIRS) in military communication has yet to be deployed.

Multimodal robotic interface for the MIRS is an interdisciplinary endeavour. This is not realistic that one can comprehend all expert and related knowledge and skills to design and develop such multimodal interactive robotic interface. In this brief preliminary survey, the author has discussed extant AI, robotics, NLP, CV, VDM, and VE applications that is directly related to multimodal interaction. Each mode of this multimodal communication is an active research area. Multimodal human/military robot communication is the ultimate goal of this research.

A multimodal autonomous robot in military communication using speech, images, gestures, VST and VE has yet to be deployed. Autonomous multimodal communication is expected to open wider possibilities for all armed forces. Given the density of the land domain, the army is in a position to exploit the opportunities for human–machine teaming (HMT) exposure. Naval and air forces will adopt platform specific suites for specially selected operators to integrate with and leverage this emerging technology. The possession of a flexible communications means that readily adapts to virtual training will enhance planning and mission rehearsals tremendously.

Interaction, perception, cognition and visualization based multimodal communication system is yet missing. Options to communicate, express and convey information in HMT setting with multiple options, suggestions and recommendations will certainly enhance military communication, strength, engagement, security, cognition, perception as well as the ability to act confidently for a successful mission.

The objective is to develop a multimodal autonomous interactive robot for military communications. This survey reports the state of the art, what exists and what is missing, what can be done and possibilities of extension that support the military in maintaining effective communication using multimodalities. There are some separate ongoing progresses, such as in machine-enabled speech, image recognition, tracking, visualizations for situational awareness, and virtual environments. At this time, there is no integrated approach for multimodal human robot interaction that proposes a flexible and agile communication. The report briefly introduces the research proposal about multimodal interactive robot in military communication.