When change is all around: How dynamic network capability and generative NPD learning shape a firm’s capacity for major innovation

Author(s):  
Yongjian (Ken) Chen ◽  
Nicole Coviello ◽  
Chatura Ranaweera
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yongjian (Ken) Chen ◽  
Nicole Coviello ◽  
Chatura Ranaweera

Purpose Systematic research examining the mechanisms that mediate the dynamic capability–performance relationship remains scarce. So too is research on the conditions under which these mechanisms might be influential. Accordingly, this study aims to build upon business network research to examine how a firm’s dynamic network capability (DNC) impacts firm performance, mediated by the speed of product reconfiguration (i.e. new product development [NPD] speed) and bounded by firm age. Design/methodology/approach The authors conduct moderated mediation analysis on survey data from small- and medium-sized manufacturing and technology firms in the USA. This study uses an initial survey and then a follow-up survey. Findings The findings support the general view that DNC is instrumental to firm performance, regardless of firm age. However, DNC operates differently for younger vs older firms. That is, DNC’s impact on the performance of younger firms is enabled by speeding up NPD, while much of the performance impact for older firms appears to be through alternative resource reconfiguration route(s). This study identifies the need to include a mediating variable such as resource reconfiguration to detect how DNC impacts performance. Research limitations/implications The model could include different dimensions of mediating resource reconfigurations, alternative boundary conditions and longer-term data. Practical implications This study provides managers with insight on how speed of product reconfiguration (in terms of NPD) operates in the DNC–performance relationship. It also helps them understand how this relationship changes in younger vs older firms. Originality/value To the best of the authors’ knowledge, this study is the first to provide empirical evidence on how DNC operates to influence performance in firms that are younger vs older.


2012 ◽  
Vol 3 (2) ◽  
pp. 419-423
Author(s):  
JARUPULA RAJESHWAR ◽  
Dr G NARSIMHA

A freely moving nodes forming as group to communicate among themselves are called as Mobile AdHoc Networks (MANET). Many applications are choosing this MANET for effective commutation due to its flexible nature in forming a network. But due to its openness characteristics it is posing many security challenges. As it has highly dynamic network topology security for routing is playing a major role. We have very good routing protocols for route discovery as well as for transporting data packers but most of them lack the feature of security like AODV. In this paper we are studying the basic protocol AODV and identify how it can be made secure. We are studying a protocol S-AODV which is a security extension of AODV which is called Secure AODV (S-AODV) and we are studying enhanced version of S-AODV routing protocol a Adaptive Secure AODV (A-SAODV). Finally we have described about the parameter to be taken for performance evaluation of different secure routing protocols


2017 ◽  
Vol 6 (2/3) ◽  
pp. 93-119
Author(s):  
Miguel Angel Gavilan-Rubio ◽  
Biliana Alexandrova-Kabadjova

2020 ◽  
Author(s):  
Michael Ellington ◽  
Jozef Barunik
Keyword(s):  

Author(s):  
Pengfei (Taylor) Li ◽  
Peirong (Slade) Wang ◽  
Farzana Chowdhury ◽  
Li Zhang

Traditional formulations for transportation optimization problems mostly build complicating attributes into constraints while keeping the succinctness of objective functions. A popular solution is the Lagrangian decomposition by relaxing complicating constraints and then solving iteratively. Although this approach is effective for many problems, it generates intractability in other problems. To address this issue, this paper presents an alternative formulation for transportation optimization problems in which the complicating attributes of target problems are partially or entirely built into the objective function instead of into the constraints. Many mathematical complicating constraints in transportation problems can be efficiently modeled in dynamic network loading (DNL) models based on the demand–supply equilibrium, such as the various road or vehicle capacity constraints or “IF–THEN” type constraints. After “pre-building” complicating constraints into the objective functions, the objective function can be approximated well with customized high-fidelity DNL models. Three types of computing benefits can be achieved in the alternative formulation: ( a) the original problem will be kept the same; ( b) computing complexity of the new formulation may be significantly reduced because of the disappearance of hard constraints; ( c) efficiency loss on the objective function side can be mitigated via multiple high-performance computing techniques. Under this new framework, high-fidelity and problem-specific DNL models will be critical to maintain the attributes of original problems. Therefore, the authors’ recent efforts in enhancing the DNL’s fidelity and computing efficiency are also described in the second part of this paper. Finally, a demonstration case study is conducted to validate the new approach.


2020 ◽  
Vol 53 (2) ◽  
pp. 1031-1036
Author(s):  
Guilherme A. Pimentel ◽  
Rafael de Vasconcelos ◽  
Aurélio Salton ◽  
Alexandre Bazanella

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