Types of embedded ties in buyer-supplier relationships and their combined effects on innovation performance

[EN] Purpose of the paper - This research analyzes the impact of three types of embedded ties, namely, specialized complementary resources, idiosyncratic investments, and knowledge sharing, on the innovation capacity of the firms. We also study the particularities of the Machine-Tool industry. Theoretical background – Our evaluation of the embedded buyer-supplier ties is based on the potential sources of relational rents proposed by Dyer and Sing (1998). We also draw on Uzzi and Lancaster (2003), Noordhoff et al. (2011), among others, to discuss the positive and negative aspects of embedded ties. Design/Methodology/Approach ‐ Using data from a survey of 202 European machine-tool firms acting as buyers and sellers, we propose and evaluate a Structural Equation model. Findings ‐ Only knowledge-sharing routines exert a significant positive effect on product innovation performance. Neither an increase in the idiosyncratic investments nor in complementary resources and capabilities enhance innovation performance. Also, knowledge-sharing routines mediate in the effect from idiosyncratic investments on innovation performance. Research Limitations. ‐ The machine tool industry has unique characteristics that make this generalization difficult. Also, there is considerable difficulty associated with testing more deeply the interrelations among these embedded ties in the long run. It is plausible to understand that these interrelations operate within a gradual process. Originality/Value/Contribution of Paper ‐ This research contributes to a better understanding of the role of embedded ties on innovativeness. To the best of our knowledge, there is no previous international empirical research analyzing the mediation effects among specialized complementary resources, idiosyncratic investments and knowledge sharing, and their effects on the innovation capacity of firms.

Distributional semantics and machine learning for statistical machine translation

[EU]Lan honetan semantika distribuzionalaren eta ikasketa automatikoaren erabilera aztertzen dugu itzulpen automatiko estatistikoa hobetzeko. Bide horretan, erregresio logistikoan oinarritutako ikasketa automatikoko eredu bat proposatzen dugu hitz-segiden itzulpen- probabilitatea modu dinamikoan modelatzeko. Proposatutako eredua itzulpen automatiko estatistikoko ohiko itzulpen-probabilitateen orokortze bat dela frogatzen dugu, eta testuinguruko nahiz semantika distribuzionaleko informazioa barneratzeko baliatu ezaugarri lexiko, hitz-cluster eta hitzen errepresentazio bektorialen bidez. Horretaz gain, semantika distribuzionaleko ezagutza itzulpen automatiko estatistikoan txertatzeko beste hurbilpen bat lantzen dugu: hitzen errepresentazio bektorial elebidunak erabiltzea hitz-segiden itzulpenen antzekotasuna modelatzeko. Gure esperimentuek proposatutako ereduen baliagarritasuna erakusten dute, emaitza itxaropentsuak eskuratuz oinarrizko sistema sendo baten gainean. Era berean, gure lanak ekarpen garrantzitsuak egiten ditu errepresentazio bektorialen mapaketa elebidunei eta hitzen errepresentazio bektorialetan oinarritutako hitz-segiden antzekotasun neurriei dagokienean, itzulpen automatikoaz haratago balio propio bat dutenak semantika distribuzionalaren arloan.