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      Digital platform capability and organizational agility of emerging market manufacturing SMEs: The mediating role of intellectual capital and the moderating role of environmental dynamism

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      Technological Forecasting and Social Change
      Elsevier BV

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          Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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            A new criterion for assessing discriminant validity in variance-based structural equation modeling

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              Sources of method bias in social science research and recommendations on how to control it.

              Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms "method" and "method bias" and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.
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                Author and article information

                Journal
                Technological Forecasting and Social Change
                Technological Forecasting and Social Change
                Elsevier BV
                00401625
                April 2022
                April 2022
                : 177
                : 121513
                Article
                10.1016/j.techfore.2022.121513
                e226b12e-dadb-4ca6-a034-08f7ecc80b77
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

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