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      Information technology, knowledge management and environmental dynamism as drivers of innovation ambidexterity: a study in SMEs

      , ,
      Journal of Knowledge Management
      Emerald

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          Abstract

          Purpose

          The purpose of this study is to assess the effect of technological, organizational and environmental factors on innovation ambidexterity and its influence on the performance of manufacturing small- and medium-sized enterprises (SMEs) as well as the moderating effect environmental dynamism on this relationship.

          Design/methodology/approach

          Drawing on the Technology–Organization–Environment theory and the Knowledge-Based View, this paper develops an integrative research model, which analyzes the network of relations using covariance-based structural equation modeling on a data set of 429 Spanish SMEs.

          Findings

          The results show that information technology capability, knowledge management capability and environmental dynamism are positively associated with innovation ambidexterity. In addition, environmental dynamism is found to strengthen the positive effect of innovation ambidexterity on firm performance.

          Practical implications

          The study findings support the idea that innovation can be developed in an ambidextrous manner within a single SME as long as the firm is capable of creating a suitable organizational context and giving a prompt response to changes in the business environment.

          Originality/value

          Although many studies have highlighted that being ambidextrous is more challenging for SMEs than for their larger counterparts, the vast majority of studies has been conducted in large companies. This paper extends prior literature by analyzing antecedents and outcomes of innovation ambidexterity in manufacturing SMEs.

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          Most cited references87

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          Common method biases in behavioral research: A critical review of the literature and recommended remedies.

          Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
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            Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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              Firm Resources and Sustained Competitive Advantage

              Jay Barney (1991)
              Understanding sources of sustained competitive advantage has become a major area of research in strategic management. Building on the assumptions that strategic resources are heterogeneously distributed acrossfirms and that these differences are stable over time, this article examines the link betweenfirm resources and sustained competitive advantage. Four empirical indicators of the potential of firm resources to generate sustained competitive advantage-value, rareness, imitability, and substitutability-are discussed. The model is applied by analyzing the potential of severalfirm resourcesfor generating sustained competitive advantages. The article concludes by examining implications of this firm resource model of sustained competitive advantage for other business disciplines.
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                Author and article information

                Journal
                Journal of Knowledge Management
                JKM
                Emerald
                1367-3270
                February 14 2018
                May 18 2018
                February 14 2018
                May 18 2018
                : 22
                : 4
                : 824-849
                Article
                10.1108/JKM-10-2017-0448
                ff907491-e980-46ed-a330-1258ca26f81e
                © 2018

                https://www.emerald.com/insight/site-policies

                History

                Quantitative & Systems biology,Biophysics
                Quantitative & Systems biology, Biophysics

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