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This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

This article deals with how to test for and evaluate interdependence among control practices in a management control system using structural equation modeling. Empirical research on the levers of control (LOC) framework is used as an example. In LOC research, a path model approach to interdependence has been developed. The appropriateness of this approach is evaluated, developed, and compared with the correlation of residuals approach (seemingly unrelated regression) implemented in the wider complementarity literature. Empirical examples of the different models are shown and compared by using a data set on LOC of 120 SBUs in Sweden. The empirical results show that modeling interdependence among control practices in a management control system as non-recursive (bi-directional) paths or as residual correlations evidently affects the conclusions drawn about interdependence in terms of both presence and magnitude. The two models imply different views on how to conceptualize interdependence and are not statistically and empirically comparable. If using non-recursive path models, several model specification issues appear. To be able to identify such models, this needs to be carefully considered in the theory and research design prior to data collection.

This chapter elaborates on the usefulness of embracing complexity theory, modeling outcomes rather than directionality, and modeling complex rather than simple outcomes in strategic management. Complexity theory includes the tenet that most antecedent conditions are neither sufficient nor necessary for the occurrence of a specific outcome. Identifying a firm by individual antecedents (i.e., noninnovative vs. highly innovative, small vs. large size in sales or number of employees, or serving local vs. international markets) provides shallow information in modeling specific outcomes (e.g., high sales growth or high profitability) – even if directional analyses (e.g., regression analysis, including structural equation modeling) indicate that the independent (main) effects of the individual antecedents relate to outcomes directionally – because firm (case) anomalies almost always occur to main effects. Examples: a number of highly innovative firms have low sales while others have high sales and a number of noninnovative firms have low sales while others have high sales. Breaking-away from the current dominant logic of directionality testing – null hypothesis significance testing (NHST) – to embrace somewhat precise outcome testing (SPOT) is necessary for extracting highly useful information about the causes of anomalies – associations opposite to expected and “statistically significant” main effects. The study of anomalies extends to identifying the occurrences of four-corner strategy outcomes: firms doing well in favorable circumstances, firms doing badly in favorable circumstances, firms doing well in unfavorable circumstances, and firms doing badly in unfavorable circumstances. Models of four-corner strategy outcomes advance strategic management beyond the current dominant logic of directional modeling of single outcomes.

The purpose of this paper is to establish a random simulation method to compare the forecasting performance between grey prediction models, and between grey model and other kinds of prediction models. Then, the different performance of three grey models and linear regression prediction model is studied, based on the proposed method.

A random simulation method was proposed to test the modelling accuracy of grey prediction model. This method was enlightened by Monte Carlo simulation method. It regarded a class of sequences as population, and selected a large sample from population though random sampling. Then, sample sequences were modeled by grey prediction model. Through modeling error calculation, the average error of grey model for the sample was obtained. Finally, the grey model accuracy for this kind of problem was acquired by statistical inference testing model. Through the statistical significant test method, the modeling accuracy of grey models for the same problem can be compared. Also, accuracy difference between grey prediction model and regression analysis, support vector machine, neural network, and other forecasting methods can be also compared.

Though random simulation experiments, the following conclusion was obtained. First, grey model can be applied to the long sequence whose growth rate was less than 20 per cent, and the short sequence whose growth rate was less than 50 per cent. Second, GM(1,1) cannot be applied to a long sequence with high growth. Third, growth rate was a more important factor than growth length on modeling accuracy of GM(1,1). Fourth, when the sequence length was short, accuracy of GM(1,1) model was higher than linear regression. While the length of the sequence was more than 15, and the growth rate in [0‐10 per cent], two kinds of modeling error was not significantly different.

The method proposed in the paper can be used to compare the performance of different prediction models, and to select appropriate model for a prediction problem.

The paper succeeded in establishing an accuracy test method for grey models and other prediction models. It will standardize the grey modelling and contribute to application of grey models.

Smart card-based E-payment systems are receiving increasing attention as the number of implementations is witnessed on the rise globally. Understanding of user adoption behavior of E-payment systems that employ smart card technology becomes a research area that is of particular value and interest to both IS researchers and professionals. However, research interest focuses mostly on why a smart card-based E-payment system results in a failure or how the system could have grown into a success. This signals the fact that researchers have not had much opportunity to critically review a smart card-based E-payment system that has gained wide support and overcome the hurdle of critical mass adoption. The Octopus in Hong Kong has provided a rare opportunity for investigating smart card-based E-payment system because of its unprecedented success. This research seeks to thoroughly analyze the Octopus from technology adoption behavior perspectives.

Cultural impacts on adoption behavior are one of the key areas that this research posits to investigate. Since the present research is conducted in Hong Kong where a majority of population is Chinese ethnicity and yet is westernized in a number of aspects, assuming that users in Hong Kong are characterized by eastern or western culture is less useful. Explicit cultural characteristics at individual level are tapped into here instead of applying generalization of cultural beliefs to users to more accurately reflect cultural bias. In this vein, the technology acceptance model (TAM) is adapted, extended, and tested for its applicability cross-culturally in Hong Kong on the Octopus. Four cultural dimensions developed by Hofstede are included in this study, namely uncertainty avoidance, masculinity, individualism, and Confucian Dynamism (long-term orientation), to explore their influence on usage behavior through the mediation of perceived usefulness.

TAM is also integrated with the innovation diffusion theory (IDT) to borrow two constructs in relation to innovative characteristics, namely relative advantage and compatibility, in order to enhance the explanatory power of the proposed research model. Besides, the normative accountability of the research model is strengthened by embracing two social influences, namely subjective norm and image. As the last antecedent to perceived usefulness, prior experience serves to bring in the time variation factor to allow level of prior experience to exert both direct and moderating effects on perceived usefulness.

The resulting research model is analyzed by partial least squares (PLS)-based Structural Equation Modeling (SEM) approach. The research findings reveal that all cultural dimensions demonstrate direct effect on perceived usefulness though the influence of uncertainty avoidance is found marginally significant. Other constructs on innovative characteristics and social influences are validated to be significant as hypothesized. Prior experience does indeed significantly moderate the two influences that perceived usefulness receives from relative advantage and compatibility, respectively. The research model has demonstrated convincing explanatory power and so may be employed for further studies in other contexts. In particular, cultural effects play a key role in contributing to the uniqueness of the model, enabling it to be an effective tool to help critically understand increasingly internationalized IS system development and implementation efforts. This research also suggests several practical implications in view of the findings that could better inform managerial decisions for designing, implementing, or promoting smart card-based E-payment system.

The purpose of this study was to develop a three-dimensional (3D) finite element modeling (FEM) technique using the commercially available program ABAQUS to predict the thermal and structural behavior of composite beams under fire loading.

The model was benchmarked using experimental test data, and it accounts for temperature-dependent material properties, force-slip-temperature relationship for the shear studs and concrete cracking.

It was determined that composite beams can be modeled with this sequentially coupled thermal-structural 3D FEM to predict the displacement versus bottom flange temperature response and associated composite beam failure modes, including compression failure in the concrete slab, runaway deflection because of yielding of the steel beam or fracture of the shear studs.

The Eurocode stress-strain-temperature (σ-ε-T) material model for structural steel and concrete conservatively predict the composite beam deflections at temperatures above 500°C. Models that use the National Institute of Standards and Technology (NIST) stress-strain-temperature (σ-ε-T) material model more closely match the measured deflection response, as compared to the results using the Eurocode model. However, in some cases, the NIST model underestimates the composite beam deflections at temperatures above 500°C.

The introductory chapter includes how to design-in good practices in theory, data collection procedures, analysis, and interpretations to avoid these bad practices. Given that bad practices in research are ingrained in the career training of scholars in sub-disciplines of business/management (e.g., through reading articles exhibiting bad practices usually without discussions of the severe weaknesses in these studies and by research courses stressing the use of regression analysis and structural equation modeling), this editorial is likely to have little impact. However, scholars and executives supporting good practices should not lose hope. The relevant literature includes a few brilliant contributions that can serve as beacons for eliminating the current pervasive bad practices and for performing highly competent research.

This chapter describes how to use popular software programs (Hierarchical Linear Modeling, LISREL) to analyze multiwave panel data. We review prevailing methods for panel data analyzes in strategic management research and identify their limitations. Then, we explain how multilevel and latent growth modeling provide more rigorous methodologies for studying dynamic phenomena. We present an example illustrating how firm performance can initiate temporal change in the human and social capital of members of Board of Directors, using hierarchical linear modeling. With the same data set, we replicate this test with first-order factor latent growth modeling (LGM). Next, we explain how to use second-order factor LGM with panel data on employee cognitions. Finally, we review the relative advantages and disadvantages of these new data-analytical approaches.