Pratyush Nidhi Sharma

Cross-National Differences in Consumer Satisfaction: Mobile Services in Emerging and Developed Markets

As firms attempt revenue growth through expansion into international markets, research on the potentially differential nature of consumer perceptions across national markets has become increasingly important. The authors advance the customer satisfaction literature by comparing customer perceptions in the wireless services industry across the national markets of Barbados, Singapore, Turkey, the United Kingdom, and the United States. This five-country context provides a unique opportunity for understanding how customers differ across markets because the data encompass consumers in disparate national markets (e.g., small/large, developing/developed, culturally heterogeneous) but include perceptions regarding a ubiquitous and increasingly commoditized service (wireless services). Focusing on emerging- versus developed-market comparisons, the findings provide important insights into unique differences in customer perceptions, including the greater importance of quality relative to value in influencing satisfaction in developed markets and the lesser importance of satisfaction in influencing customer loyalty in emerging markets.

A Comparison of PLS and ML Bootstrapping Techniques in SEM: A Monte Carlo Study

Structural Equation Modeling (SEM) techniques have been extensively used in business and social science research to model complex relationships. The two most widely used estimation methods in SEM are the Maximum Likelihood (ML) and Partial Least Square (PLS). Both the estimation methods rely on Bootstrap re-sampling to a large extent. While PLS relies completely on Bootstrapping to obtain standard errors for hypothesis testing, ML relies on Bootstrapping under conditions in violation of the distributional assumptions. Even though Bootstrapping has several advantages, it may fail under certain conditions. In this Monte Carlo study, we compare the accuracy and efficiency of ML and PLS based Bootstrapping in SEM, while recovering the true estimates under various conditions of sample size and distributional assumptions. Our results suggest that researchers might benefit by using PLS based bootstrapping with smaller sample sizes. However, at larger sample sizes the use of ML based bootstrapping is recommended.

Factors Influencing the Planned Adoption of Continuous Monitoring Technology

The article discusses a study which explored the key factors that influence an organizations plan to adopt continuous monitoring technology (CMT). A brief introduction to the Unified Theory of Acceptance and Use of Technology (UTAUT) framework used in the study is presented. Findings suggest that the key perception that drives whether a non-adopter decides to adopt CMT is performance expectancy. It also revealed that effort expectancy is a non-significant factor.

Examining Turnover in Open Source Software Projects Using Logistic Hierarchical Linear Modeling Approach

Developer turnover in open source software projects is a critical and insufficiently researched problem. Previous research has focused on understanding the developer motivations to contribute using either the individual developer perspective or the project perspective. In this exploratory study we argue that because the developers are embedded in projects it is imperative to include both perspectives. We analyze turnover in open source software projects by including both individual developer level factors, as well as project specific factors. Using the Logistic Hierarchical Linear Modeling approach allows us to empirically examine the factors influencing developer turnover and also how these factors differ among developers and projects.

Predictive model selection in partial least squares path modeling (PLS-PM)

Predictive model selection metrics are used to select models with the highest out-of-sample predictive power among a set of models. R2 and related metrics, which are heavily used in partial least squares path modeling, are often mistaken as predictive metrics. We introduce information theoretic model selection criteria that are designed for out-of-sample prediction and which do not require creating a holdout sample. Using a Monte Carlo study, we compare the performance of frequently used model evaluation criteria and information theoretic criteria in selecting the best predictive model under various conditions of sample size, effect size, loading patterns, and data distribution.

Model Misspecifications and Bootstrap Parameter Recovery in PLS-SEM and CBSEM-Based Exploratory Modeling

Theories are uncertain and evolving in exploratory research. This uncertainty can manifest itself in SEM studies either at the measurement or structural level, or both, and result in model misspecifications. Researchers often favor the use of PLS-SEM over CBSEM in exploratory research due to its tractability, flexibility, and its ability to avoid factor indeterminacy. While these strengths of PLS-SEM are undoubtedly appealing, empirical support regarding the robustness of model parameters under conditions of model misspecifications is lacking. This Monte Carlo study evaluates the efficiency and accuracy of bootstrap parameter recovery by PLS-SEM, CBSEM, and the Bollen-Stine methods under various conditions of measurement and structural misspecification effect sizes, sample sizes, and data distributions. Results point to the favorability of PLS-SEM in exploratory modeling when structural parameters are of interest, while CBSEM and Bollen-Stine methods are appealing when the focus is at the measurement level. A two-pronged strategy is advisable when theoretical uncertainty exists both at the measurement and structural levels.