Coopetition and international entrepreneurship: the influence of a competitor orientation

PurposeGuided by a relational, stakeholder perspective of resource-based theory, the purpose of the current investigation is to help unpack the complexity of the performance-enhancing nature of coopetition for international entrepreneurs, namely the interplay between collaboration and competition. The context features under-resourced wine producers owned and managed by entrepreneurs that have implemented an internationalised business model. The focus of the study involves the influence of a “competitor orientation”, namely when decision-makers understand the short-term strengths, weaknesses, long-term capabilities and strategies of key current and potential rivals.Design/methodology/approachData collection primarily featured semi-structured interviews with owner-managers of wine-producing firms in New Zealand that reflected heterogeneity amongst international entrepreneurs' strategies targeting different product markets within their respective business models. Secondary data were also collected where possible. Specifically, interviewees' firms exhibited different portfolios involving wine sales (with varying export intensities) together with augmented sales of tourism-related products/services focussed on the domestic market.FindingsCoopetition activities amongst international entrepreneurs varied; i.e. influenced by respective owner-managers' competitor orientations. Illustrations of different decision-makers' business models within a 2 × 2 matrix feature those with a low- or high-export intensity, together with a narrow or augmented product portfolio. Internationalising entrepreneurs' perceptions varied regarding the extent to which their respective business model was oriented towards local cluster-based domestic tourism with limited export sales, as opposed to those with national and more importantly international wine sales. Possessing and acting upon relevant knowledge manifested in which competitors international entrepreneurs collaborated with and the extent to which this took place across product-market strategies. In turn, this enabled particular decision-makers to exhibit flexibility; hence, entrepreneurs enter and exit certain markets together with changing export intensities, as varying opportunities were identified and exploited.Originality/valueAlthough the performance-enhancing nature of coopetition is largely established in prior literature, the complexity of that relationship remains relatively under-researched, not least, amongst international entrepreneurs. More specifically, the extent to which decision-makers that are engaged in coopetition exhibit a competitor orientation remains under-researched. Unique insights feature a 2 × 2 matrix in order to provide originality regarding international entrepreneurs' respective product-market strategies within their business models that are underpinned by varying coopetition relationships and competitor orientations.

Clustering and visualization of single-cell RNA-seq data using path metrics

Recent advances in single-cell technologies have enabled high-resolution characterization of tissue and cancer compositions. Although numerous tools for dimension reduction and clustering are available for single-cell data analyses, these methods often fail to simultaneously preserve local cluster structure and global data geometry. This article explores the application of power-weighted path metrics for the analysis of single cell RNA data. Extensive experiments on single cell RNA sequencing data sets confirm the usefulness of path metrics for dimension reduction and clustering. Distances between cells are measured in a data- driven way which is both density sensitive (decreasing distances across high density regions) and respects the underlying data geometry. By combining path metrics with multidimensional scaling, a low dimensional embedding of the data is obtained which respects both the global geometry of the data and preserves cluster structure. We evaluate the method both for clustering quality and geometric fidelity, and it outperforms other algorithms on a wide range of bench marking data sets.

GLOBAL SUPPLY CHAIN RELATIONSHIP, LOCAL MARKET COMPETITION, AND SUPPLIERS’ INNOVATION IN DEVELOPING ECONOMIES

This article examines how suppliers’ innovation in developing countries is affected by the interaction of vertical global supply chain relationships and horizontal market competition structure. We devised a bidirectional dynamic game model consisting of competing suppliers in a developing economy and an overseas buyer in a developed economy for innovation decision process in a suppliers cluster. Our research shows that global supply chain relationship is the primary factor to influence local cluster innovation and profit. Total innovation of the cluster is proved to be greater in global supply relationship with a powerful buyer than a non-powerful buyer. However, suppliers in a powerful buyer chain are not able to capture the value they created from innovation. Local competition structure plays its secondary role on cluster innovation through interaction with vertical chain relationship. Based on prior innovation research on either vertical supply chain power dynamics or horizontal competition intenseness, our study contributes as the first to employ a theoretical suppliers’ innovation model for an integrative analysis encompassing both global and local power dynamics.

Local modelling approach for cross-project defect prediction

Prediction approaches used for cross-project defect prediction (CPDP) are usually impractical because of high false alarms, or low detection rate. Instance based data filter techniques that improve the CPDP performance are time-consuming and each time a new test set arrives for prediction the entire filter procedure is repeated. We propose to use local modeling approach for the utilization of ever-increasing cross-project data for CPDP. We cluster the cross-project data, train per cluster prediction models and predict the target test instances using corresponding cluster models. Over 7 NASA Data sets performance comparison using statistical methods between within-project, cross-project, and our local modeling approach were performed. Compared to within-project prediction the cross-project prediction increased the probability of detection (PD) associated with an increase in the probability of false alarm (PF) and decreased overall performance Balance. The application of local modeling decreased the (PF) associated with a decrease in (PD) and an overall performance improvement in terms of Balance. Moreover, compared to one state of the art filter technique – Burak filter, our approach is simple, fast, performance comparable, and opens a new perspective for the utilization of ever-increasing cross-project data for defect prediction. Therefore, when insufficient within-project data is available we recommend training local cluster models than training a single global model on cross-project datasets.

GASP and MaNGA Surveys Shed Light on the Enigma of the Gas Metallicity Gradients in Disk Galaxies

Making use of both MUSE observations of 85 galaxies from the survey GASP (GAs Stripping Phenomena in galaxies with MUSE) and a large sample from MaNGA (Mapping Nearby Galaxies at Apache Point Observatory survey), we investigate the distribution of gas metallicity gradients as a function of stellar mass for local cluster and field galaxies. Overall, metallicity profiles steepen with increasing stellar mass up to 1010.3 M ⊙ and flatten out at higher masses. Combining the results from the metallicity profiles and the stellar mass surface density gradients, we propose that the observed steepening is a consequence of local metal enrichment due to in situ star formation during the inside-out formation of disk galaxies. The metallicity gradient−stellar mass relation is characterized by a rather large scatter, especially for 109.8 < M ⋆/M ⊙ < 1010.5, and we demonstrate that metallicity gradients anti-correlate with the galaxy gas fraction. Focusing on the galaxy environment, at any given stellar mass, cluster galaxies have systematically flatter metallicity profiles than their field counterparts. Many subpopulations coexist in clusters: galaxies with shallower metallicity profiles appear to have fallen into their present host halo sooner and have experienced the environmental effects for a longer time than cluster galaxies with steeper metallicity profiles. Recent galaxy infallers, like galaxies currently undergoing ram pressure stripping, show metallicity gradients more similar to those of field galaxies, suggesting they have not felt the effect of the cluster yet.

Disruption of Hierarchical Clustering in the Vela OB2 Complex and the Cluster Pair Collinder 135 and UBC 7 with Gaia EDR3: Evidence of Supernova Quenching

We identify hierarchical structures in the Vela OB2 complex and the cluster pair Collinder 135 and UBC 7 with Gaia EDR3 using the neural network machine-learning algorithm StarGO. Five second-level substructures are disentangled in Vela OB2, which are referred to as Huluwa 1 (Gamma Velorum), Huluwa 2, Huluwa 3, Huluwa 4, and Huluwa 5. For the first time, Collinder 135 and UBC 7 are simultaneously identified as constituent clusters of the pair with minimal manual intervention. We propose an alternative scenario in which Huluwa 1–5 have originated from sequential star formation. The older clusters Huluwa 1–3, with an age of 10–22 Myr, generated stellar feedback to cause turbulence that fostered the formation of the younger-generation Huluwa 4–5 (7–20 Myr). A supernova explosion located inside the Vela IRAS shell quenched star formation in Huluwa 4–5 and rapidly expelled the remaining gas from the clusters. This resulted in global mass stratification across the shell, which is confirmed by the regression discontinuity method. The stellar mass in the lower rim of the shell is 0.32 ± 0.14 M ⊙ higher than in the upper rim. Local, cluster-scale mass segregation is observed in the lowest-mass cluster Huluwa 5. Huluwa 1–5 (in Vela OB2) are experiencing significant expansion, while the cluster pair suffers from moderate expansion. The velocity dispersions suggest that all five groups (including Huluwa 1A and Huluwa 1B) in Vela OB2 and the cluster pair are supervirial and are undergoing disruption, and also that Huluwa 1A and Huluwa 1B may be a coeval young cluster pair. N-body simulations predict that Huluwa 1–5 in Vela OB2 and the cluster pair will continue to expand in the future 100 Myr and eventually dissolve.

Guardiola, Klopp, and Pochettino: The Purveyors of What? The Use of Passing Network Analysis to Identify and Compare Coaching Styles in Professional Football

We applied social networks analysis to objectively discriminate and describe interpersonal interaction dynamics of players across different top-coaching styles. The aim was to compare metrics in the passing networks of Jürgen Klopp, Pep Guardiola, and Mauricio Pochettino across the UEFA Champions League seasons from 2017 to 2020. Data on completed passes from 92 games were gathered and average passing networks metrics were computed. We were not only able to find the foundations on which these elite coaches build the passing dynamics in their respective teams, but also to determine important differences that represent their particular coaching signatures. The local cluster coefficient was the only metric not significantly different between coaches. Still, we found higher average shortest-path length for Guardiola's network (mean ± std = 3.00 ± 0.45 a.u.) compared to Klopp's (2.80 ± 0.52 a.u., p = 0.04) and Pochettino's (2.70 ± 0.39 a.u., p = 0.01). Density was higher for Guardiola's (64.16 ± 20.27 a.u.) than for Pochettino's team (51.42 ± 17.28 a.u., p = 0.008). The largest eigenvalue for Guardiola's team (65.95 ± 16.79 a.u.) was higher than for Klopp's (47.06 ± 17.25 a.u., p &lt; 0.001) and Pochettino's (42,62 ± 12.01 a.u., p &lt; 0.001). Centrality dispersion was also higher for Guardiola (0.14 ± 0.02 a.u.) when compared to Klopp (0.12 ± 0.03 a.u., p = 0.008). The local cluster coefficient seems to build the foundation for passing work, however, cohesion characteristics among players in the three teams of the top coaches seems to characterize their own footprint regarding passing dynamics. Guardiola stands out by the high number of passes and the enhanced connection of the most important players in the network. Klopp and Pochettino showed important similarities, which are associated to preferences toward more flexibility of interpersonal linkages synergies.

Local clustering via approximate heat kernel PageRank with subgraph sampling

Graph clustering, a fundamental technique in network science for understanding structures in complex systems, presents inherent problems. Though studied extensively in the literature, graph clustering in large systems remains particularly challenging because massive graphs incur a prohibitively large computational load. The heat kernel PageRank provides a quantitative ranking of nodes, and a local cluster can be efficiently found by performing a sweep over the heat kernel PageRank vector. But computing an exact heat kernel PageRank vector may be expensive, and approximate algorithms are often used instead. Most approximate algorithms compute the heat kernel PageRank vector on the whole graph, and thus are dependent on global structures. In this paper, we present an algorithm for approximating the heat kernel PageRank on a local subgraph. Moreover, we show that the number of computations required by the proposed algorithm is sublinear in terms of the expected size of the local cluster of interest, and that it provides a good approximation of the heat kernel PageRank, with approximation errors bounded by a probabilistic guarantee. Numerical experiments verify that the local clustering algorithm using our approximate heat kernel PageRank achieves state-of-the-art performance.

Differences in Foreign Language Choice of Students from Different Social Backgrounds

According to previous research (e.g. Bernstein, 1971; Gogolin, 2014; Hegedűs et al., 2019), family background plays a decisive role in an individual's mother tongue acquisition and in learning foreign languages. In another study, parents with a high social background (54.0%) chose German for their children, and parents with a low social background (56.9%) chose English in primary school (Sebestyén, 2021). Based on this, in the study I examine what difference can be detected in the foreign language choice of high school students from different social backgrounds. In the study, I analyze the student data (890 people) of my database entitled “German learning and teaching in Hajdú-Bihar and Szabolcs-Szatmár-Bereg counties” prepared in the 2018/2019 school year, during which I perform cross-tabulation and cluster analysis with the help of SPSS program. The database contains data on 11th grade high school and vocational high school students who studied German and / or English in high school. As the results, there are differences between the learned foreign languages among secondary school students according to family background. Among the clusters related to high school choice, those belonging to the “Higher Education Oriented Local” cluster are most interested in foreign languages, most German-speaking (74.0%) and English (89,0%) students tend to be in this cluster. Overall, the majority of respondents learn English, while students from higher social backgrounds (also) learn German.