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Modern statistics provides an ever-expanding toolkit for estimating unknown parameters. Consequently, applied statisticians frequently face a difficult decision: retain a parameter estimate from a familiar method or replace it with an estimate from a newer or more complex one. While it is traditional to compare estimates using risk, such comparisons are rarely conclusive in realistic settings. In response, we propose the "c-value" as a measure of confidence that a new estimate achieves smaller loss than an old estimate on a given dataset. We show that it is unlikely that a large c-value coincides with a larger loss for the new estimate. Therefore, just as a small p-value supports rejecting a null hypothesis, a large c-value supports using a new estimate in place of the old. For a wide class of problems and estimates, we show how to compute a c-value by first constructing a data-dependent high-probability lower bound on the difference in loss. The c-value is frequentist in nature, but we show that it can provide validation of shrinkage estimates derived from Bayesian models in real data applications involving hierarchical models and Gaussian processes. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Over the years, the retail sector has been showing increasing interest in inculcating sustainable consumption practices and behaviors among their consumers. This study, accordingly, explores how retailers can contribute to addressing consumers' various psychological dispositions to co-create sustainable consumption in the retail context. In that regard, the MAPED framework has been developed corresponding to White et al.'s SHIFT framework for exploring the process and mechanism of co-creating sustainable consumption in the retail sector. This comprehensive conceptual framework has been developed using a methodological approach of critical review and analysis of the extant academic literature on value co-creation and sustainable consumption. Accordingly, this study attempts to contribute to both sustainable consumption and value co-creation literature. [ABSTRACT FROM AUTHOR]

Commercial marketing literature highlights benefits from brand advocates who recruit and promote in the interest of the commercial entity. However, a similar focus is lacking on how advocacy can extend the effectiveness of social change initiatives. We utilise a case study to demonstrate the benefit of social advocacy and its impact on behaviour change, and thereby propose an advocacy model. To develop this conceptual model, we discuss several key areas; behaviour change and advocacy, advocate identification, and how to influence advocacy within communities and individuals. This research provides a guiding framework for practitioners to develop programs and interventions with advocacy triggers and strategies to enhance the longevity and effectiveness of social change programs through participant-based advocacy. Thus, giving intervention programs in a variety of organisational structures e.g. non-profit, corporate, government etc. a specific model to increase the effectiveness of social programs. Our paper extends behaviour change literature by leveraging social marketing concepts to modify and extend the transtheoretical model. [ABSTRACT FROM AUTHOR]

We introduce a three-step framework to determine at which pitches Major League batters should swing. Unlike traditional plate discipline metrics, which implicitly assume that all batters should always swing at (resp. take) pitches inside (resp. outside) the strike zone, our approach explicitly accounts not only for the players and umpires involved in the pitch but also in-game contextual information like the number of outs, the count, baserunners, and score. We first fit flexible Bayesian nonparametric models to estimate (i) the probability that the pitch is called a strike if the batter takes the pitch; (ii) the probability that the batter makes contact if he swings; and (iii) the number of runs the batting team is expected to score following each pitch outcome (e.g. swing and miss, take a called strike, etc.). We then combine these intermediate estimates to determine whether swinging increases the batting team's run expectancy. Our approach enables natural uncertainty propagation so that we can not only determine the optimal swing/take decision but also quantify our confidence in that decision. We illustrate our framework using a case study of pitches faced by Mike Trout in 2019. [ABSTRACT FROM AUTHOR]