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Increased globalization, varying customer requirements, extended product lines, uncertainty regarding supplier performance, and myriad related factors make supply chains utterly complex. While previous research indicates that supply chain complexity plays an important role in explaining performance outcomes, the accumulating evidence is ambiguous. Thus, a finer‐grained analysis is required. By meta‐analyzing 27,668 observations across 102 independent samples from 123 empirical studies, we examine the link between supply chain complexity and firm performance. While the preponderance of evidence from previous studies identifies supply chain complexity as detrimental to firm performance, our results illustrate that although supply chain complexity has a negative effect on operational performance, it has a positive effect on innovation performance and financial performance. Furthermore, we also distinguish among different levels of supply chain (i.e., upstream, downstream, and internal) and observe nuanced findings. Finally, our findings also reveal moderating effects of construct operationalization and study design characteristics. We discuss implications for theory and practice and provide avenues for future research.

Mental simulation of future scenarios is hypothesized to affect future behavior, but a large and inconsistent literature means it is unclear whether, and under what conditions, mental simulation can change people’s behavior. A meta-analysis was conducted to synthesize the effects of mental simulation on behavior and examine under what conditions mental simulation works best. An inclusive systematic database search identified 123 (N = 5,685) effect sizes comparing mental simulation to a control group. After applying a multilevel random effects model, a statistically-reliable positive effect of Hedges’ g = 0.49, 95% CI [0.37; 0.62] was found, which was significantly different than zero. Using a taxonomy to identify different subtypes of mental simulation (along two dimensions, class [process, performance, outcome] and purpose [whether an inferior, standard, superior version of that behavior is simulated]), it was found that superior simulations garnered more reliable beneficial effects than inferior simulations. These findings have implications for integrating theories of how mental simulations change behavior, how mental simulations are classified, and may help guide professionals seeking evidence-based and cost-effective methods of changing behavior. Supplementary Information The online version contains supplementary material available at 10.3758/s13423-021-01880-6.

textabstractWe study the relationship between supplier involvement in new product development and performance. The current literature is scattered and fragmented with studies reporting mixed empirical evidence for a variety of concepts related to “Early Supplier Involvement.” We conduct a systematic review and meta-analysis of the existing literature to reconcile conflicted findings, revise and refine theoretical perspectives, and provide evidence-based scholarly and practical implications. To achieve these aims, we unravel the general relationship by considering three factors. First, we delineate different types of performance outcomes, mainly related to NPD efficiency (e.g., speed) and NPD effectiveness (e.g., product quality). Second, we distinguish between the moment and the extent of supplier involvement, related to different theoretical perspectives on external knowledge integration. Third, we disentangle multiple levels of analysis that are seemingly obscured in the literature, specifically the project and organizational levels. We find that extensive supplier involvement has positive effects on NPD efficiency and effectiveness, whereas earlier supplier involvement only to some degree affects NPD efficiency and not effectiveness. In conclusion, our meta-analysis based on 11,420 observations from 51 studies provides strong theoretical and practical insights on the important phenomenon of supplier involvement.

Increased globalization, pressure to meet varying customer needs and requirements, extended product lines, uncertainty in supplier deliveries and a myriad of related factors make supply chains utterly complex. Extant literature shows that Supply Chain Complexity (SCC) plays an important role in explaining several performance outcomes. However, the accumulating evidence is ambiguous. To better understand SCC and its consequences under specific boundary conditions, we conducted a meta-analytical synthesis of the extant literature. Meta- analytic results from 23,810 observations across 85 independent samples from 103 empirical studies indicated that rather than an aggregate analysis of SCC’s effect on performance, a finer-grained analysis of the relationships between sub-components of both SCC and performance is required. While the majority of previous studies seem to advocate that complexity in supply chains is detrimental to firm performance, our results demonstrate that SCC only has a negative effect on operational performance, but an overall positive effect on innovation performance and financial performance. We further decompose SCC into sub-dimensions (i.e. upstream/downstream/internal) illustrating how exactly complexity in the supply chain of a firm affects its performance across a range of relevant outcomes. Our findings also reveal moderating effects of construct operationalization and study characteristics. We discuss the implications of the findings for theory and practice."

We present a new tool for meta?analysis, Meta?Essentials, which is free of charge and easy to use. In this paper, we introduce the tool and compare its features to other tools for meta?analysis. We also provide detailed information on the validation of the tool. Although free of charge and simple, Meta?Essentials automatically calculates effect sizes from a wide range of statistics and can be used for a wide range of meta?analysis applications, including subgroup analysis, moderator analysis, and publication bias analyses. The confidence interval of the overall effect is automatically based on the Knapp?Hartung adjustment of the DerSimonian?Laird estimator. However, more advanced meta?analysis methods such as meta?analytical structural equation modelling and meta?regression with multiple covariates are not available. In summary, Meta?Essentials may prove a valuable resource for meta?analysts, including researchers, teachers, and students. Metrics Article has an altmetric score of 19 Details © 2017 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.

Meta-analysis is a systematic method for synthesizing quantitative results of different empirical studies regarding the effect of an independent variable (or determinant, or intervention, or treatment) on a defined outcome (or dependent variable). Mainly developed in medical and psychological research as a tool for synthesizing empirical information about the outcomes of a treatment, meta-analysis is now increasingly used in the social sciences as a tool for hypothesis testing. However, the assumptions underlying meta-analytic hypothesis testing in the social sciences will usually not be met under real-life conditions. This is the reason why meta-analysis is increasingly conducted with a different aim, based on more realistic assumptions. That aim is to explore the dispersion of effect sizes.