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Journal ArticleDOI

Drivers of innovativeness and performance for innovative SMEs in South Korea: Mediation of learning orientation

01 Jan 2010-Technovation (Elsevier)-Vol. 30, Iss: 1, pp 65-75
TL;DR: In this paper, the authors investigated the relationship between drivers of innovativeness and the mediation effects of learning orientation and found that market orientation and entrepreneurial orientation significantly influenced learning orientation, respectively.
About: This article is published in Technovation.The article was published on 2010-01-01. It has received 642 citations till now. The article focuses on the topics: Entrepreneurial orientation & Market orientation.
Citations
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Journal ArticleDOI
TL;DR: In this article, a systematic analysis of the entrepreneurship literature is conducted in order to take stock of the theoretical and empirical development and identify research themes and developmental patterns of EL research, including individual and collective learning, exploratory and exploitative learning, and intuitive and sensing learning.
Abstract: Entrepreneurial learning (EL) has emerged as an important concept at the interface of entrepreneurship and organizational learning. Although EL research has gained momentum in the past decade, the literature is diverse, highly individualistic and fragmented, hindering the development of EL as a promising research area. In this paper, a systematic analysis of the EL literature is first conducted in order to take stock of the theoretical and empirical development and identify research themes and developmental patterns of EL research. Second, three pairs of key learning types that deserve more attention in future research are discussed, namely individual and collective learning, exploratory and exploitative learning, and intuitive and sensing learning. These learning types correspond to three key challenges that are derived from the EL research gaps identified in the systematic literature analysis, and provide fruitful avenues for future research. Third, by exploring the three pairs of learning types, further insights are drawn from entrepreneurship and organizational learning to help to advance EL research, and also feed back to the entrepreneurship literature by discussing how these learning types can help to understand the challenges at the centre of debate in the entrepreneurship literature.

426 citations


Cites background from "Drivers of innovativeness and perfo..."

  • ...In particular, studies in the EE context have already started to explore how the learning process and the entrepreneurial process interact to have impact on firm performance (e.g. Covin et al. 2006; Hughes et al. 2007; Rhee et al. 2010; Wang 2008; Zhao et al. 2011)....

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Journal ArticleDOI
Henri Hakala1
TL;DR: In this article, the authors present a systematic review of this literature, covering 67 scholarly articles published between 1987 and 2010 which investigate multiple orientations, including market, technology, entrepreneurial and learning orientations.
Abstract: Market, technology, entrepreneurial and learning orientations have attracted major scholarly interest within their specific streams of literature for some decades. These strategic orientations are seen as principles that direct and influence the activities of a firm and generate the behaviours intended to ensure its viability and performance. Prior studies have argued that firms should develop and use multiple orientations, yet the relationship between different orientations has received only fragmented attention. This paper presents a systematic review of this literature, covering 67 scholarly articles published between 1987 and 2010 which investigate multiple orientations. The paper contributes first by summarizing the current state of knowledge on the interplay between these orientations. Many of these relationships have not been studied to any great degree, and there are research gaps in the information available on the relationships between entrepreneurial, technology and learning orientation in particular. Secondly, the paper contributes to further theoretical and empirical enquiry by synthesizing the empirical findings into a three-approach framework. The sequential, alternatives and complementary approaches to perceiving the relationship between orientations all suggest areas for further research. The sequential approach could further contribute by developing better constructs for explaining the orientation of the firm; while the alternatives approach could increase its relevance to management through the exploration of contingency settings and comparative studies. The complementary approach encourages discussion between researchers from the different streams of literature through the investigation of the relationships. It suggests focus on the investigation of both universal- and contingency-dependent-orientation configurations.

404 citations

Journal ArticleDOI
TL;DR: The empirical research was conducted on 175 small to medium enterprises in the United Kingdom, suggesting that the knowledge-driven approach is the strongest determinant, leading to a preference for informal inbound OI modes.
Abstract: Purpose This paper aims to investigate three key factors (i.e. cognitive dimensions, the knowledge-driven approach and absorptive capacity) that are likely to determine the preference for informal inbound open innovation (OI) modes, through the lens of the OI model and knowledge-based view (KBV). The innovation literature has differentiated these collaborations into informal inbound OI entry modes and formal inbound OI modes, offering an advocative and conceptual view. However, empirical studies on these collaborations are still limited. Design/methodology/approach Building on the above-mentioned theoretical framework, the empirical research was performed in two stages. First, data were collected via a closed-ended questionnaire distributed to all the participants from the sample by e-mail. Second, to assess the hypotheses, structural equation modelling (SEM) via IBM® SPSS® Amos 20 was applied. Findings The empirical research was conducted on 175 small to medium enterprises in the United Kingdom, suggesting that the knowledge-driven approach is the strongest determinant, leading to a preference for informal inbound OI modes. The findings were obtained using SEM and are discussed in line with the theoretical framework. Research limitations/implications Owing to the chosen context and sector of the empirical analysis, the research results may lack generalisability. Hence, new studies are proposed. Practical implications The paper includes implications for the development of informal inbound OI led by knowledge-driven approach. Originality/value This paper offers an empirical research to investigate knowledge-driven preferences in informal inbound OI modes.

260 citations


Cites background from "Drivers of innovativeness and perfo..."

  • ...This is because SMEs are likely to be more innovation oriented (Martinez-Conesa et al., 2017; Keskin, 2006; Rhee et al., 2010; Rosenbusch et al., 2011; Salavou and Lioukas, 2003)....

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Journal ArticleDOI
TL;DR: In this paper, the authors used survey data from a sample of 154 high-tech manufacturing firms in Taiwan and employed hierarchical moderated regression analysis to test the hypotheses developed to clarify the nature of the effect of firm innovativeness on business performance.

232 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the interplay between customer orientation, innovation, and business performance in the Alpine hospitality industry and found that the effect of hotels' customer orientation exceeds the effects of innovativeness and innovation behavior on financial and non-financial business performance.

223 citations

References
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Journal ArticleDOI
TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...
Abstract: The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addit...

56,555 citations

Book
01 Jan 1983
TL;DR: In this Section: 1. Multivariate Statistics: Why? and 2. A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques.
Abstract: In this Section: 1. Brief Table of Contents 2. Full Table of Contents 1. BRIEF TABLE OF CONTENTS Chapter 1 Introduction Chapter 2 A Guide to Statistical Techniques: Using the Book Chapter 3 Review of Univariate and Bivariate Statistics Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis Chapter 5 Multiple Regression Chapter 6 Analysis of Covariance Chapter 7 Multivariate Analysis of Variance and Covariance Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures Chapter 9 Discriminant Analysis Chapter 10 Logistic Regression Chapter 11 Survival/Failure Analysis Chapter 12 Canonical Correlation Chapter 13 Principal Components and Factor Analysis Chapter 14 Structural Equation Modeling Chapter 15 Multilevel Linear Modeling Chapter 16 Multiway Frequency Analysis 2. FULL TABLE OF CONTENTS Chapter 1: Introduction Multivariate Statistics: Why? Some Useful Definitions Linear Combinations of Variables Number and Nature of Variables to Include Statistical Power Data Appropriate for Multivariate Statistics Organization of the Book Chapter 2: A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques Some Further Comparisons A Decision Tree Technique Chapters Preliminary Check of the Data Chapter 3: Review of Univariate and Bivariate Statistics Hypothesis Testing Analysis of Variance Parameter Estimation Effect Size Bivariate Statistics: Correlation and Regression. Chi-Square Analysis Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis Important Issues in Data Screening Complete Examples of Data Screening Chapter 5: Multiple Regression General Purpose and Description Kinds of Research Questions Limitations to Regression Analyses Fundamental Equations for Multiple Regression Major Types of Multiple Regression Some Important Issues. Complete Examples of Regression Analysis Comparison of Programs Chapter 6: Analysis of Covariance General Purpose and Description Kinds of Research Questions Limitations to Analysis of Covariance Fundamental Equations for Analysis of Covariance Some Important Issues Complete Example of Analysis of Covariance Comparison of Programs Chapter 7: Multivariate Analysis of Variance and Covariance General Purpose and Description Kinds of Research Questions Limitations to Multivariate Analysis of Variance and Covariance Fundamental Equations for Multivariate Analysis of Variance and Covariance Some Important Issues Complete Examples of Multivariate Analysis of Variance and Covariance Comparison of Programs Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures General Purpose and Description Kinds of Research Questions Limitations to Profile Analysis Fundamental Equations for Profile Analysis Some Important Issues Complete Examples of Profile Analysis Comparison of Programs Chapter 9: Discriminant Analysis General Purpose and Description Kinds of Research Questions Limitations to Discriminant Analysis Fundamental Equations for Discriminant Analysis Types of Discriminant Analysis Some Important Issues Comparison of Programs Chapter 10: Logistic Regression General Purpose and Description Kinds of Research Questions Limitations to Logistic Regression Analysis Fundamental Equations for Logistic Regression Types of Logistic Regression Some Important Issues Complete Examples of Logistic Regression Comparison of Programs Chapter 11: Survival/Failure Analysis General Purpose and Description Kinds of Research Questions Limitations to Survival Analysis Fundamental Equations for Survival Analysis Types of Survival Analysis Some Important Issues Complete Example of Survival Analysis Comparison of Programs Chapter 12: Canonical Correlation General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Canonical Correlation Some Important Issues Complete Example of Canonical Correlation Comparison of Programs Chapter 13: Principal Components and Factor Analysis General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Factor Analysis Major Types of Factor Analysis Some Important Issues Complete Example of FA Comparison of Programs Chapter 14: Structural Equation Modeling General Purpose and Description Kinds of Research Questions Limitations to Structural Equation Modeling Fundamental Equations for Structural Equations Modeling Some Important Issues Complete Examples of Structural Equation Modeling Analysis. Comparison of Programs Chapter 15: Multilevel Linear Modeling General Purpose and Description Kinds of Research Questions Limitations to Multilevel Linear Modeling Fundamental Equations Types of MLM Some Important Issues Complete Example of MLM Comparison of Programs Chapter 16: Multiway Frequency Analysis General Purpose and Description Kinds of Research Questions Limitations to Multiway Frequency Analysis Fundamental Equations for Multiway Frequency Analysis Some Important Issues Complete Example of Multiway Frequency Analysis Comparison of Programs

53,113 citations

Journal ArticleDOI
01 Jan 1973
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each. Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations. Table of Contents 1. Introduction. I. PREPARING FOR A MULTIVARIATE ANALYSIS. 2. Examining Your Data. 3. Factor Analysis. II. DEPENDENCE TECHNIQUES. 4. Multiple Regression. 5. Multiple Discriminant Analysis and Logistic Regression. 6. Multivariate Analysis of Variance. 7. Conjoint Analysis. 8. Canonical Correlation Analysis. III. INTERDEPENDENCE TECHNIQUES. 9. Cluster Analysis. 10. Multidimensional Scaling. IV. ADVANCED AND EMERGING TECHNIQUES. 11. Structural Equation Modeling. 12. Emerging Techniques in Multivariate Analysis. Appendix A: Applications of Multivariate Data Analysis. Index.

37,124 citations

Journal ArticleDOI
TL;DR: In this paper, the authors provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development, and present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests.
Abstract: In this article, we provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development. We present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests. We discuss the comparative advantages of this approach over a one-step approach. Considerations in specification, assessment of fit, and respecification of measurement models using confirmatory factor analysis are reviewed. As background to the two-step approach, the distinction between exploratory and confirmatory analysis, the distinction between complementary approaches for theory testing versus predictive application, and some developments in estimation methods also are discussed.

34,720 citations

Journal ArticleDOI
TL;DR: In this paper, the authors argue that the ability of a firm to recognize the value of new, external information, assimilate it, and apply it to commercial ends is critical to its innovative capabilities.
Abstract: In this paper, we argue that the ability of a firm to recognize the value of new, external information, assimilate it, and apply it to commercial ends is critical to its innovative capabilities. We label this capability a firm's absorptive capacity and suggest that it is largely a function of the firm's level of prior related knowledge. The discussion focuses first on the cognitive basis for an individual's absorptive capacity including, in particular, prior related knowledge and diversity of background. We then characterize the factors that influence absorptive capacity at the organizational level, how an organization's absorptive capacity differs from that of its individual members, and the role of diversity of expertise within an organization. We argue that the development of absorptive capacity, and, in turn, innovative performance are history- or path-dependent and argue how lack of investment in an area of expertise early on may foreclose the future development of a technical capability in that area. We formulate a model of firm investment in research and development (R&D), in which R&D contributes to a firm's absorptive capacity, and test predictions relating a firm's investment in R&D to the knowledge underlying technical change within an industry. Discussion focuses on the implications of absorptive capacity for the analysis of other related innovative activities, including basic research, the adoption and diffusion of innovations, and decisions to participate in cooperative R&D ventures. **

31,623 citations