Search results

The purpose of this article is to understand how academics in management deal with the concept of generation in the workplace. We begin by conducting an interdisciplinary literature analysis, thereby elaborating a conceptual framework concerning generational diversity. This framework consists of four levels of analysis (society, career, organisation and occupation) and three dimensions (age, cohort and event/period). We then conduct a meta-analysis using this conceptual framework to analyse papers from the management field. The results from this analysis reveal the existence of a diversity of generational approaches, which focus on the dimensions of age and cohort on a societal level. Four factors seem to explain these results: the recent de-synchronisation of generational dimensions and levels, the novelty of theoretical models, the amplification of stereotypes by mass media and the methodologies employed by researchers. In sum, this article contributes to a more realistic view of generational diversity in the workplace for both academics and practitioners.

Comparing rural development with agricultural modernisation, there are fundamental differences. Industrial development of agriculture more and more segregates agriculture from other functions and is based on an ‘individualised transaction model’ in which the world consists of loose particles that are linked by markets (atomistic world view). Conversely rural development can be perceived as a form of re-socialisation of agriculture and is based on a ‘relational cooperation model’ in which new relations characterise business development.

This chapter is a second level type of analysis of many research findings of these common traits or features and gives a picture of the distinctiveness of rural development practices. Nine different features that characterize rural development practices are described and discussed: (1) novelty production, (2) relative autonomy, (3) synergy, (4) clashes and competing claims, (5) coalitions and new relations; the construction of rural webs, (6) common pool resources, (7) new division of labour, (8) the distinctive different impact and (9) resilience. The more these features are present and intertwined, the better the specific practice can face and withstand adverse conditions. These features and the associated practices have to be understood as part of a wider transitional process that might co-evolve with or run counter to competing transitional processes.

Additive manufacturing (AM) is a promising alternative to the conventional production methods (i.e., machining), providing the developers with great geometrical and topological freedom during the design and immediate prototyping customizability. However, frictional characteristics of the AM surfaces are yet to be fully explored, making the control and manufacturing of precise assembly manufactured mechanisms (i.e., robots) challenging. The purpose of this paper is to understand the tribological behavior of fused deposition modeling (FDM) manufactured surfaces and test the accuracy of existing mathematical models such as Amontons–Coulomb, Tabor–Bowden, and variations of Hertz Contact model against empirical data.

Conventional frictional models Amontons–Coulomb and Tabor–Bowden are developed for the parabolic surface topography of FDM surfaces using variations of Hertz contact models. Experiments are implemented to measure the friction between two flat FDM surfaces at different speeds, normal forces, and surface configuration, including the relative direction of printing stripes and sliding direction and the surface area. The global maximum measured force is considered as static friction, and the average of the local maxima during the stick-slip phase is assumed as kinematic friction. Spectral analysis has been used to inspect the relationship between the chaos of vertical wobbling versus sliding speed.

It is observed that the friction between the two FDM planes is linearly proportional to the normal force. However, in contrast to the viscous frictional model (i.e., Stribeck), the friction reduces asymptotically at higher speeds, which can be attributed to the transition from harmonic to normal chaotic vibrations. The phase shift is investigated through spectral analysis; dominant frequencies are presented at different pulling speeds, normal forces, and surface areas. It is hypothesized that higher speeds lead to smaller dwell-time, reducing creep and adhesive friction consequently. Furthermore, no monotonic relationship between surface area and friction force is observed.

Due to the high number of experimental parameters, the research is implemented for a limited range of surface areas, which should be expanded in future research. Furthermore, the pulling position of the jaws is different from the sliding distance of the surfaces due to the compliance involved in the contact and the pulling cable. This issue could be alleviated using a non-contact position measurement method such as LASER or image processing. Another major issue of the experiments is the planar orientation of the pulling object with respect to the sliding direction and occasional swinging in the tangential plane.

Given the results of this study, one can predict the frictional behavior of FDM manufactured surfaces at different normal forces, sliding speeds, and surface configurations. This will help to have better predictive and model-based control algorithms for fully AM manufactured mechanisms and optimization of the assembly manufactured systems. By adjusting the clearances and printing direction, one can reduce or moderate the frictional forces to minimize stick-slip or optimize energy efficiency in FDM manufactured joints. Knowing the harmonic to chaotic phase shift at higher sliding speeds, one can apply certain speed control algorithms to sustain optimal mechanical performance.

In this study, theoretical tribological models are developed for the specific topography of the FDM manufactured surfaces. Experiments have been implemented for an extensive range of boundary conditions, including normal force, sliding speed, and contact configuration. Frictional behavior between flat square FDM surfaces is studied and measured using a Zwick tensile machine. Spectral analysis, auto-correlation, and other methods have been developed to study the oscillations during the stick-slip phase, finding local maxima (kinematic friction) and dominant periodicity of the friction force versus sliding distance. Precise static and kinematic frictional coefficients are provided for different contact configurations and sliding directions.

This chapter describes the change efforts and action research projects at a Dutch multinational which, over a period of 25 years, produced in one of its businesses a zigzag path toward collaborative leadership dynamics at the horizontal and vertical interfaces. The chapter also identifies the learning mechanisms that helped achieve this transformation. Changing the patterns at the vertical interfaces proved to be a most tricky, complex, and confusing operation. The data show that organizations need hierarchical interfaces between levels, but are hindered by the hierarchical leadership dynamics at these interfaces. The data furthermore show that competitive performance requires more than redesigning horizontal interfaces. A business can only respond with speed and flexibility to threats and opportunities in the external environment when the leadership dynamics at agility-critical vertical interfaces are also changed.

The purpose of this paper is to present a generic pipeline for Resource Description Framework (RDF) graph mining to provide a comprehensive review of each step in the knowledge discovery from data process. The authors also investigate different approaches and combinations to extract feature vectors from RDF graphs to apply the clustering and theme identification tasks.

The proposed methodology comprises four steps. First, the authors generate several graph substructures (Walks, Set of Walks, Walks with backward and Set of Walks with backward). Second, the authors build neural language models to extract numerical vectors of the generated sequences by using word embedding techniques (Word2Vec and Doc2Vec) combined with term frequency-inverse document frequency (TF-IDF). Third, the authors use the well-known K-means algorithm to cluster the RDF graph. Finally, the authors extract the most relevant rdf:type from the grouped vertices to describe the semantics of each theme by generating the labels.

The experimental evaluation on the state of the art data sets (AIFB, BGS and Conference) shows that the combination of Set of Walks-with-backward with TF-IDF and Doc2vec techniques give excellent results. In fact, the clustering results reach more than 97% and 90% in terms of purity and F-measure, respectively. Concerning the theme identification, the results show that by using the same combination, the purity and F-measure criteria reach more than 90% for all the considered data sets.

The originality of this paper lies in two aspects: first, a new machine learning pipeline for RDF data is presented; second, an efficient process to identify and extract relevant graph substructures from an RDF graph is proposed. The proposed techniques were combined with different neural language models to improve the accuracy and relevance of the obtained feature vectors that will be fed to the clustering mechanism.