Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships. A common goal of PLS-SEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior. Building on an introduction of the fundamentals of measurement and structural theory, this chapter explains how to specify and estimate path models using PLS-SEM. Complementing the introduction of the PLS-SEM method and the description of how to evaluate analysis results, the chapter also offers an overview of complementary analytical techniques. A PLS-SEM application of the widely recognized corporate reputation model illustrates the method.

Partial Least Squares Structural Equation Modeling

Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity – which includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement error – renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

On making causal claims : A review and recommendations

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From Control to Commitment in the Workplace: In Factory after Factory, There Is a Revolution Under Way in the Management of Work. Readings on Labor-Management Relations.

Summary
Statistical control is widely used in correlational studies with the intent of providing more accurate estimates of relationships among variables, more conservative tests of hypotheses, or ruling out alternative explanations for empirical findings. However, the use of control variables can produce uninterpretable parameter estimates, erroneous inferences, irreplicable results, and other barriers to scientific progress. As a result, methodologists have provided a great deal of advice regarding the use of statistical control, to the point that researchers might have difficulties sifting through and prioritizing the available suggestions. We integrate and condense this literature into a set of 10 essential recommendations that are generally applicable and which, if followed, would substantially enhance the quality of published organizational research. We provide explanations, qualifications, and examples following each recommendation. Copyright © 2015 John Wiley & Sons, Ltd.

Statistical control in correlational studies: 10 essential recommendations for organizational researchers.

Effect size information is essential for the scientific enterprise and plays an increasingly central role in the scientific process. We extracted 147,328 correlations and developed a hierarchical taxonomy of variables reported in Journal of Applied Psychology and Personnel Psychology from 1980 to 2010 to produce empirical effect size benchmarks at the omnibus level, for 20 common research domains, and for an even finer grained level of generality. Results indicate that the usual interpretation and classification of effect sizes as small, medium, and large bear almost no resemblance to findings in the field, because distributions of effect sizes exhibit tertile partitions at values approximately one-half to one-third those intuited by Cohen (1988). Our results offer information that can be used for research planning and design purposes, such as producing better informed non-nil hypotheses and estimating statistical power and planning sample size accordingly. We also offer information useful for understanding the relative importance of the effect sizes found in a particular study in relationship to others and which research domains have advanced more or less, given that larger effect sizes indicate a better understanding of a phenomenon. Also, our study offers information about research domains for which the investigation of moderating effects may be more fruitful and provide information that is likely to facilitate the implementation of Bayesian analysis. Finally, our study offers information that practitioners can use to evaluate the relative effectiveness of various types of interventions.

Correlational effect size benchmarks

Using different measures of constructs in research to develop robust evidence of relationships and effects is seen as good methodological practice. This assumes these measures possess high convergent validity. However, proxies—alternative measures of the same construct—are rarely perfectly convergent. Although some convergence is preferred to none, this study demonstrates that even modest departures from perfect convergent validity can result in substantial differences in the magnitudes of findings, creating challenges for the accumulation and interpretation of research. Using data from published research, the authors find that substantial differences in findings between studies using desired and proxy variables occur even at levels of convergent validity as high as r = .85. Implications of using measures with less-than-ideal convergent validity for the interpretability of research results are examined. Convergent validities above r = .70 are recommended, whereas those below r = .50 should be avoided. Res...

Understanding the Impact of Convergent Validity on Research Results

This study provides an organizational level investigation of the determinants of perceptions of diversity climate among employees. In total, 3,578 employees across 163 hotels provided data. Measures of diversity programs, managerial values and diversity climate were drawn from independent groups of employees. Support was found for the relationship between the deployment of diversity programs and diversity climate. This relationship was moderated by the actual diversity and the collective relational values of the management teams. Further, collective managerial relational values were found to be predictive of the adoption of diversity initiatives. This study provides evidence that though the presence of diversity initiatives was associated with higher levels of diversity climate, this relationship is not straightforward. Organizational attention to contextual factors, including managerial values and levels of minority representation in management, is necessary to enhance the efficacy of these programs. In spite of growth in practitioner and scientific attention, few conclusions are possible regarding the influence of organizational diversity on organizational level outcomes (Jackson et al., J Manage 29:801–830, 2003). This study responds to recent evidence that diversity climate moderates the relationship between diversity and organizational performance and answers calls for empirical attention to understanding how diversity climates are created and managed (Gonzalez and DeNisi, J Organ Behav 30(1):21–40, 2009).

Establishing a Diversity Program is Not Enough: Exploring the Determinants of Diversity Climate

How Does Leader-Member Exchange Disparity Affect Teamwork Behavior and Effectiveness in Work Groups? The Moderating Role of Leader-Leader Exchange

What explains why high-performance work systems (HPWSs) are not adopted more widely by firms that would appear to benefit economically by adopting them? We address this question by drawing on the upper-echelons perspective to consider the role of the top managers’ beliefs concerning the financial payoffs from investments in HR (“HR cause-effect belief”) as well as their employee-centered value-based beliefs (labeled “HPWS values”). We propose a conceptual model in which top management HPWS values moderate the relationship between HR cause-effect beliefs and the intensity of HPWS programs reported by managers as well as the relationship between HPWS programs and employees’ perceptions of implemented HPWS practices. We test our model using a unique multisource data set collected from 120 hotel franchisees that includes survey responses from 648 managers and 1,293 employees. We find that firms’ ability to translate top managers’ cause-effect beliefs about the economic value of HR investments into adoption of HPWS programs, as well as their ability to effectively implement these HPWS programs, is significantly affected by the value-based HR beliefs held by top managers.

How Top Management HR Beliefs and Values Affect High-Performance Work System Adoption and Implementation Effectiveness

The authors contribute to growing evidence that team charters contribute positively to performance by empirically testing their effects on key team process outcomes. Using a sample of business students in a team-based task requiring significant cooperative and coordinative behavior, the authors compare emergent team norms under a variety of team charter intervention conditions. They find support for the assertion that the introduction of team charters does in fact manifest improved process outcomes, including communication, effort, mutual support, cohesion, and member satisfaction.

Andrew O. Herdman

Papers

Understanding the Impact of Convergent Validity on Research Results

Establishing a Diversity Program is Not Enough: Exploring the Determinants of Diversity Climate

How Does Leader-Member Exchange Disparity Affect Teamwork Behavior and Effectiveness in Work Groups? The Moderating Role of Leader-Leader Exchange

How Top Management HR Beliefs and Values Affect High-Performance Work System Adoption and Implementation Effectiveness

The Effects of a Team Charter on Student Team Behaviors