This study investigates how knowledge sharing (KS) contributes to firm performance (FP) through the enhancement of innovation and/or intellectual capital (IC) using data collected from Chinese high-technology firms. The paper proposes three alternative models that suggest different mediating roles of innovation and IC components in the KSFP nomological network based on existing theory. The paper then compares these models in terms of in-sample explanatory and out-of-sample predictive powers using consistent partial least squares path modeling (PLSc). Results indicate that in the best performing model, innovation and IC simultaneously mediate the relationship between KS and FP in this specific context. The findings offer insights regarding the parallel mediation roles of innovation and IC in the KSFP process, showcase the predictive utility of PLSc, and can help managers set priorities when leveraging KS to achieve specific performance goals.

Retailers seek to utilize both online and offline purchase channels strategically to satisfy customers and thrive in the marketplace. Unfortunately, current multichannel research is deficient in answering what drives customers' satisfaction, and consequently their loyalty, differently when customers purchase online versus at a physical store. This gap in knowledge can be a significant concern for retailers due to the negative impact of having dissatisfied customers on their bottom lines. Using a version of the American Customer Satisfaction Index (ACSI) model, we demonstrate several important purchase-channel differences in the antecedents of customer satisfaction and its subsequent effect on customer loyalty. Specifically, we show that when retail customers buy electronic goods online they view purchase value as a significant attribute in rating satisfaction, and are more satisfaction-sensitive when making repurchase decisions than when they purchase offline. On the other hand, the overall quality of the purchase experience and customer expectations are stronger drivers of customer satisfaction in the offline purchases. We provide evidence that these differences between the channels generally persist across customer demographics (gender, age, and education) and broader product categories, and we also discuss the specific contexts where they do not. Our work offers actionable guidance to retailers seeking to enhance customer satisfaction and loyalty across both the online and offline channels.

Partial least squares path modeling (PLS-PM) has become popular in various disciplines to model structural relationships among latent variables measured by manifest variables. To fully benefit from the predictive capabilities of PLS-PM, researchers must understand the efficacy of predictive metrics used. In this research, we compare the performance of standard PLS-PM criteria and model selection criteria derived from Information Theory, in terms of selecting the best predictive model among a cohort of competing models. We use Monte Carlo simulation to study this question under various sample sizes, effect sizes, item loadings, and model setups. Specifically, we explore whether, and when, the in-sample measures such as the model selection criteria can substitute for out-of-sample criteria that require a holdout sample. Such a substitution is advantageous when creating a holdout causes considerable loss of statistical and predictive power due to an overall small sample. We find that when the researcher does not have the luxury of a holdout sample, and the goal is selecting correctly specified models with low prediction error, the in-sample model selection criteria, in particular the Bayesian Information Criterion (BIC) and Geweke-Meese Criterion (GM), are useful substitutes for out-of-sample criteria. When a holdout sample is available, the best performing out-of-sample criteria include the root mean squared error (RMSE) and mean absolute deviation (MAD). Finally, we recommend against using standard the PLS-PM criteria (R 2 , Adjusted R 2 , and Q 2), and specifically the out-of-sample mean absolute percentage error (MAPE) for prediction-oriented model selection purposes. Finally, we illustrate the model selection criteria's practical utility using a well-known corporate reputation model.

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Management researchers often develop theories and policies that are forward‐looking. The prospective outlook of predictive modeling, where a model predicts unseen or new data, can complement the retrospective nature of causal‐explanatory modeling that dominates the field. Partial least squares (PLS) path modeling is an excellent tool for building theories that offer both explanation and prediction. A limitation of PLS, however, is the lack of a statistical test to assess whether a proposed or alternative theoretical model offers significantly better out‐of‐sample predictive power than a benchmark or an established model. Such an assessment of predictive power is essential for theory development and validation, and for selecting a model on which to base managerial and policy decisions. We introduce the cross‐validated predictive ability test (CVPAT) to conduct a pairwise comparison of predictive power of competing models, and substantiate its performance via multiple Monte Carlo studies. We propose a stepwise predictive model comparison procedure to guide researchers, and demonstrate CVPAT's practical utility using the well‐known American Customer Satisfaction Index (ACSI) model.