The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power and the need for goodness-of-fit testing. Next, the metrics as well as the rules of thumb that should be applied to assess the PLS-SEM results are covered. Besides presenting established PLS-SEM evaluation criteria, the overview includes the following new guidelines: PLSpredict (i.e., a novel approach for assessing a model’s out-of-sample prediction), metrics for model comparisons, and several complementary methods for checking the results’ robustness.,This paper provides an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLS-SEM.,Most of the previously applied metrics for evaluating PLS-SEM results are still relevant. Nevertheless, scholars need to be knowledgeable about recently proposed metrics (e.g. model comparison criteria) and methods (e.g. endogeneity assessment, latent class analysis and PLSpredict), and when and how to apply them to extend their analyses.,Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but must always be up to date with the latest developments in the PLS-SEM method.,In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions.

https://www.emerald.com/insight/content/doi/10.1108/EBR-11-2018-0203/full/pdf

When to use and how to report the results of PLS-SEM

Despite the numerous observations that dynamic capabilities lie at the source of competitive advantage, we still have limited knowledge as to how access to firm-based resources and changes to these affect the development of dynamic capabilities. In this paper, we examine founder human capital, access to employee human capital, access to technological expertise, access to other specific expertise, and access to two types of tangible resources in a sample of new firms in Sweden. We empirically measure four dynamic capabilities and find that the nature and effect of resources employed in the development of these capabilities vary greatly. For the most part, there are positive effects stemming from access to particular resources. However, for some resources, such as access to employee human capital and access to financial capital, unexpected negative effects also appear. This study therefore provides statistical evidence as to the varying role of resources in capability development. Importantly, we also find that changes in resource bases have more influential roles in the development of dynamic capabilities than the resource stock variables that were measured at an earlier stage of firm development. This provides empirical support for the notion of treating the firm as a dynamic flow of resources as opposed to a static stock. This finding also highlights the importance of longitudinal designs in studies of dynamic capability development. Further recommendations for future empirical studies of dynamic capabilities are presented.

From resource base to dynamic capabilities : an investigation of new firms

Knowledge sharing has become an integral part of organizations’ business strategies, along with aiding organizations to grow and innovate in the market, and gain competitive advantage. This paper aims to concentrate on the role of tacit knowledge sharing in fostering innovation capability of an organization. Specifically, the study considers social capital (relational, cognitive and structural) as an important precursors to tacit knowledge sharing, which in turn, influences innovation capability of an organization. The study further discusses the role that knowledge reciprocation plays in successful tacit knowledge sharing. The relation between knowledge quality and innovation capability is also discussed in the paper.,The investigation started with a review of extant literature in the field of knowledge sharing and innovation to derive a set of constructs. A set of hypotheses was developed based on the identified constructs, which was subsequently validated through a primary survey based on a structured questionnaire on a sample size of 190 respondents from the Indian industrial domain. The survey responses were subsequently analysed using the statistical technique of structural equation modeling and conclusions were drawn from the findings. Additionally, careful attention was paid in eliminating the common method bias, which is often associated with a primary survey.,A set of six hypotheses were derived based on the identified constructs and were subsequently validated. While validating the hypotheses, it was observed that while knowledge reciprocity, relational social capital and cognitive social capital was positive associated with tacit knowledge sharing, structural social capital did not have a significant effect on the same. Additionally, it was also observed that both tacit knowledge sharing and the quality of knowledge were positively associated with innovation capability.,The present day business marked by intense competition requires firms to be more aware of their innovative capabilities. Effective sharing of knowledge or information can be deemed as a vital component in achieving this objective. Organizations that practice and nurture innovation activities can use the findings of the current study as a part of their knowledge management strategy. In addition to using the explicit knowledge, which are structured in nature, organizations can also start using tacit knowledge to harness their innovation potential – and the findings from the current study can act as a motivational tool for them to do so.,Although there is a growing body of literature concerning the role of knowledge management in innovation, there still a dearth in discussing the role of tacit knowledge sharing in exploiting the innovation capability of an organization. The main discussion of this paper brings together a set of important constructs that exhibits the significant role that tacit knowledge sharing plays in determining the innovation capability of an organization. Furthermore, it tries to marry the concepts of social capital and tacit knowledge sharing with innovation capability, therefore adding significantly to the body of literature in knowledge management as well as innovation.

Evaluating the role of social capital, tacit knowledge sharing, knowledge quality and reciprocity in determining innovation capability of an organization

In order to adhere to the concept of sustainable development, firms are increasingly expected to develop innovations that reconcile economic, environmental, and social goals (i.e., sustainable innovations). However, achieving this goal is not straightforward, and although several studies have attempted to improve our understanding of sustainable innovation, a systematization of extant findings is lacking. Therefore, this paper conducts a literature review with the objective of organizing previous research regarding sustainable innovation. A systematic approach is adopted, identifying 69 relevant articles. These articles are organized according to three key perspectives: internal managerial, external relational, and performance evaluation. The results demonstrate that the first perspective, incorporating diverse managerial aspects, is the most considered, whereas the second and third perspectives remain underdeveloped. The paper concludes with suggestions for future research.

Understanding sustainable innovation: A systematic literature review

The purpose of this paper is to examine when and how organizations build big data analytics capability (BDAC) to improve supply chain agility (SCA) and gain competitive advantage.,The authors grounded the theoretical framework in two perspectives: the dynamic capabilities view and contingency theory. To test the research hypotheses, the authors gathered 173 usable responses using a pre-tested questionnaire.,The results suggest that BDAC has a positive and significant effect on SCA and competitive advantage. Further, the results support the hypothesis that organizational flexibility (OF) has a positive and significant moderation effect on the path joining BDAC and SCA. However, contrary to the belief, the authors found no support for the moderation effect of OF on the path joining BDAC and competitive advantage.,The study makes some useful contributions to the literature on BDAC, SCA, OF, and competitive advantage. Moreover, the results may further motivate future scholars to replicate the findings using longitudinal data.

Big data analytics capability in supply chain agility: The moderating effect of organizational flexibility

Tesis de investigacion presentada como requisito para optar al titulo de: Magister en Administracion MSc. -- Linea de Investigacion: Gestion del Conocimiento y Capacidades Dinamicas.

La gestión del conocimiento y su influencia en las capacidades dinámicas: Contrastación empírica en Empresas Colombianas Intensivas en uso de conocimiento

Restrepo Guzmán

Papers

La gestión del conocimiento y su influencia en las capacidades dinámicas: Contrastación empírica en Empresas Colombianas Intensivas en uso de conocimiento