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Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy between explanation and prediction. However, while researchers using PLS-SEM routinely stress the predictive nature of their analyses, model evaluation assessment relies exclusively on metrics designed to assess the path model’s explanatory power. Recent research has proposed PLSpredict, a holdout sample-based procedure that generates case-level predictions on an item or a construct level. This paper offers guidelines for applying PLSpredict and explains the key choices researchers need to make using the procedure.

The authors discuss the need for prediction-oriented model evaluations in PLS-SEM and conceptually explain and further advance the PLSpredict method. In addition, they illustrate the PLSpredict procedure’s use with a tourism marketing model and provide recommendations on how the results should be interpreted. While the focus of the paper is on the PLSpredict procedure, the overarching aim is to encourage the routine prediction-oriented assessment in PLS-SEM analyses.

The paper advances PLSpredict and offers guidance on how to use this prediction-oriented model evaluation approach. Researchers should routinely consider the assessment of the predictive power of their PLS path models. PLSpredict is a useful and straightforward approach to evaluate the out-of-sample predictive capabilities of PLS path models that researchers can apply in their studies.

Future research should seek to extend PLSpredict’s capabilities, for example, by developing more benchmarks for comparing PLS-SEM results and empirically contrasting the earliest antecedent and the direct antecedent approaches to predictive power assessment.

This paper offers clear guidelines for using PLSpredict, which researchers and practitioners should routinely apply as part of their PLS-SEM analyses.

This research substantiates the use of PLSpredict. It provides marketing researchers and practitioners with the knowledge they need to properly assess, report and interpret PLS-SEM results. Thereby, this research contributes to safeguarding the rigor of marketing studies using PLS-SEM.

In the current era, characterized by volatility, uncertainty, complexity, and ambiguity (VUCA), organizations are becoming increasingly more complex and less formal. Consequently, traditional control processes are being replaced by knowledge-sharing processes, informal coordination, and networks. Hence, different leadership theories and methods, which are more in line with these developments, are required. Terms such as “leadership in the plural,” “emergent leadership,” “leadership as a structural and networked phenomenon” reflect changes in how the author understands the phenomenon of leadership and sets the direction for new approaches. This chapter describes four paradigm shifts from the traditional approach to leadership, which highlighted the role of a formal leader who influences a group of followers. The author presents a stream of research emphasizing a relational approach among multiple individuals and reframe leadership as an influence action of many. These influence exchanges result in an emergent influence pattern or a leadership configuration. Nevertheless, the author sought to not “throw the baby out with the bathwater,” therefore the author claims that the formal leader is embedded in this configuration. Building on social network analysis and recently developed methodologies, the author provides a platform for measuring leadership as a many-on-many influence process. The author depicts the research she conducted analyzing advice networks, while aspiring to create a synthesis between the traditional and emergent leadership approaches. At the practical level, to understand and develop leadership in organizations nowadays, the author suggests acquiring a “broad and multi-focal lens” to capture the complexity of leadership.

Purpose: To outline recent developments in digital service delivery in order to encourage researchers to pursue collaborations with computer science, operations research, and data science colleagues and to show how such collaborations can expand the scope of research on emotion in service delivery.

Design/methodology/approach: Uses archived resources available at http://LivePerson.com to extract data based in genuine service conversations between agents and customers. We refer to these as “digital traces” and analyze them using computational science models.

Findings: Although we do not test significance or causality, the data presented in this chapter provide a unique lens into the dynamics of emotions in service; results that are not obtainable using traditional research methods.

Research limitations/implications: This is a descriptive study where findings unravel new dynamics that should be followed up with more research, both research using traditional experimental methods, and digital traces research that allows inferences of causality.

Practical implications: The digital data and newly developed tools for sentiment analyses allow exploration of emotions in large samples of genuine customer service interactions. The research provides objective, unobtrusive views of customer emotions that draw directly from customer expressions, with no self-report intervention and biases.

Originality/value: This is the first objective and detailed depiction of the actual emotional encounters that customers express, and the first to analyze in detail the nature and content of customer service work.