Use The Resources Below To Assist Highlight At Least One Thi

Use The Resources Below To Assist Highlight At Least One Threat To In

Use the resources below to assist. Highlight at least one threat to internal or external validity or reliability. Explore why this threat is problematic and how it could be remedied. Comment on the articles discussed by your classmates. Consider which validity threats are most common. Which are the most difficult to deal with? Juhani Ukko, Minna Saunila & Tero Rantala (2020) Connecting relational mechanisms to performance measurement in a digital service supply chain, Production Planning & Control, 31:2-3, DOI: 10.1080/.2019.

Paper For Above instruction

The assessment task involves critically analyzing potential threats to validity and reliability within research studies, exemplified through a specific article. A key threat identifiable in the article by Ukko, Saunila, and Rantala (2020) is threats to internal validity, particularly concerning causal relationships between relational mechanisms and performance measurement in digital service supply chains. Internal validity pertains to the accuracy of causal inferences drawn from the study; if compromised, the study’s conclusions about the impact of relational mechanisms may be misleading or invalid.

In the context of this article, a prominent threat to internal validity is confounding variables. These are extraneous variables that could influence both the independent variables (relational mechanisms) and the dependent variables (performance metrics). For example, organizational culture or managerial expertise could affect both the strength of relational mechanisms and overall performance, confounding the observed relationships. If such confounders are not adequately controlled, the study’s causality claims become suspect.

This threat is particularly problematic because it challenges the core objective of such research—to establish causality rather than just correlation. If confounding variables are overlooked, the findings risk overestimating or misrepresenting the true effect of relational mechanisms. The remedy involves implementing rigorous research designs such as randomized controlled trials where feasible, or using statistical controls like multivariate regression analysis to account for potential confounders. Additionally, employing longitudinal designs can help observe changes over time, helping infer causality more confidently.

Beyond internal validity, external validity or generalizability is also a concern, especially given the specificity of digital service supply chains. Results from one organizational context or industry may not extrapolate broadly. To enhance external validity, researchers should ensure a diverse sample and replicate studies across different settings.

Moreover, reliability, referring to the consistency of measurement tools and procedures, can be threatened if measurement instruments are not standardized or validated. For example, if performance metrics are subjective or poorly defined, results may fluctuate across different contexts or observers, reducing the reliability of the findings. This could be mitigated by developing clear operational definitions, using validated measurement scales, and training data collectors thoroughly.

In analyzing classmates' articles, it becomes evident that most common threats include measurement errors, confounding variables, and issues related to sample selection. These threats are frequently difficult to address because they require careful research design, adequate resources, and sometimes longitudinal data collection. For instance, controlling for confounders demands comprehensive understanding of all relevant extraneous variables, which is often challenging in complex supply chain research.

In conclusion, while threats like confounding variables pose significant challenges to internal validity, and measurement errors threaten reliability, addressing these issues necessitates meticulous research design and methodological rigor. Recognizing and mitigating these threats enhances the credibility of research findings, ultimately advancing our understanding of complex phenomena such as performance in digital service supply chains.

References

  • Ukko, J., Saunila, M., & Rantala, T. (2020). Connecting relational mechanisms to performance measurement in a digital service supply chain. Production Planning & Control, 31(2-3), 189–202. https://doi.org/10.1080/09537287.2019.1697716
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