The necessary condition analysis (NCA) has become a prominent method for identifying must-have factors required for an outcome. With increasing sample sizes, identifying such must-have factors becomes difficult as extreme responses are more likely to occur. Addressing this concern, we introduce a novel method, the NCA with an effect size sensitivity extension (NCA-ESSE), which allows researchers to better understand the sensitivity of the NCA results to extreme response patterns. We offer guidelines for the NCA-ESSE method’s use and illustrate its efficacy using a well-known job satisfaction model. By extending NCA’s capabilities to assess the sensitivity of necessary conditions, our research enhances the method’s practical utility and helps ensure the robustness and replicability of its outcomes and conclusions.

This paper aims to promote research on Global Virtual Teams (GVTs). We review current GVT research, introduce the X-Culture data set as one potential empirical data set for future research, and summarise relevant future research avenues. These avenues align with the European Journal of International Management's (EJIM) vision of becoming a research platform for GVT-related research.

This 2-hour seminar introduces the powerful combination of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA). When used together, these methods enable a more nuanced understanding of causal structures, distinguishing between should-have and must-have factors for achieving a desired outcome. Participants will gain foundational insights into both methods, while showing how the integration works directly in the SmartPLS software environment.

Die Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM) hat sich in der wirtschafts- und sozialwissenschaftlichen Forschung als geeignetes Verfahren zur Schätzung von Kausalmodellen behauptet. Dank der Anwenderfreundlichkeit des Verfahrens und der vorhandenen Software ist es auch in der Praxis etabliert.

Recent research on partial least squares structural equation modeling (PLS–SEM) extended the classic importance–performance map analysis (IPMA) by taking the results of a necessary condition analysis (NCA) into consideration. By also highlighting necessary conditions, the combined importance–performance map analysis (cIPMA) offers a tool that enables better prioritization of management actions to improve a key target construct. In this article, we showcase a cIPMA’s main steps when using the SmartPLS 4 software. Our illustration draws on the technology acceptance model (TAM) used in the cIPMA’s original publication, which features prominently in business research.

Drawing upon the mobile technology acceptance model (MTAM), this study addresses this conspicuous gap by identifying the necessary conditions for fostering trust and usage intention of mobile payment (m-payment) gateways. Data collected from 218 users of mobile-payment from Malaysia were analyzed using partial least squares structural equation modeling (PLS-SEM) to identify the should-have factors for perceived trust and intention to use mobile payment gateways. In addition, necessary condition analyses (NCA) are utilized to identify the must-have factors for perceived trust and intention to use mobile payment gateways. Results revealed that other factors of mobile usefulness, perceived security, and user mobility explained perceived trust except for mobile ease of use. In turn, mobile usefulness, user mobility, perceived security, mobile ease of use, and perceived trust established a positive relationship with intention to use. The NCA complemented these results showing that mobile usefulness, mobile ease of use, and user mobility qualified as necessary and sufficient conditions for perceived trust with medium-sized effect and mobile usefulness is qualified as a medium-sized necessary and sufficient condition for intention to use. This is the first few article that extends earlier literature by offering arguments on the different aspects of MTAM. By using NCA, we provide theoretical and methodological evidences, we identify which are the essential ones that enhances trust and usage.

Die Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM) hat sich in der wirtschafts- und sozialwissenschaftlichen Forschung als geeignetes Verfahren zur Schätzung von Kausalmodellen behauptet. Dank der Anwenderfreundlichkeit des Verfahrens und der vorhandenen Software ist es auch in der Praxis etabliert.

This research offers a novel approach that extends the application of importance-performance map analysis (IPMA) in partial least squares structural equation modeling (PLS-SEM) by incorporating findings from a necessary condition analysis (NCA). The IPMA comprises assessing latent variables and their indicators' importance and performance, while an NCA introduces an additional dimension by identifying factors that are crucial for achieving the desired outcomes. An NCA employs necessity logic to identify the must-have factors required for an outcome, while PLS-SEM follows an additive sufficiency logic to identify the should-have factors that contribute to high performance levels. Integrating these two logics into the performance dimension is particularly valuable for prioritizing actions that could improve the target outcomes, such as customer satisfaction and employee commitment. Although the combined use of PLS-SEM and NCA is a recent suggestion, this study is the first to combine them with an IPMA (i.e., in a combined IPMA; cIPMA). A case study illustrates the combined use of PLS-SEM and an NCA to undertake a cIPMA. This innovative approach enhances researchers' and practitioners' decision making, enabling them to prioritize their efforts effectively.

Migration is one of the most pressing global issues of our time. However, relatively little is known about the factors and mechanisms that govern the post-migration experiences of skilled migrants. We adopt an acculturation- and social identity-based approach to examine how differences between institutional characteristics in the destination and origin country, as well as migrants’ experiences with formal and informal institutions shape their identification with the destination and origin country and contribute to their community and career embeddedness. Our study of 1709 highly skilled migrants from 48 origin countries in 12 destination countries reveals that the institutional environment migrants encounter provides both sources of opportunity (potential for human development and value-congruent societal practices) and sources of disadvantage (experienced ethnocentrism and downgrading). These contrasting dynamics affect migrants’ destination-country identification, their origin-country identification and, ultimately, their embeddedness in the destination country. Our results have important implications for multinational enterprises and policy makers that can contribute to enhancing skilled migrants’ community and career embeddedness. For example, these actors may nurture a work environment and provide supportive policies that buffer against the institutional sources of disadvantage we identified in this study, while helping migrants to leverage the opportunities available in the destination country.

Die Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM) hat sich in der wirtschafts- und sozialwissenschaftlichen Forschung als geeignetes Verfahren zur Schätzung von Kausalmodellen behauptet. Dank der Anwenderfreundlichkeit des Verfahrens und der vorhandenen Software ist es auch in der Praxis etabliert.

Die Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM) hat sich in der wirtschafts- und sozialwissenschaftlichen Forschung als geeignetes Verfahren zur Schätzung von Kausalmodellen behauptet. Dank der Anwenderfreundlichkeit des Verfahrens und der vorhandenen Software ist es auch in der Praxis etabliert.

Die Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM) hat sich in der wirtschafts- und sozialwissenschaftlichen Forschung als geeignetes Verfahren zur Schätzung von Kausalmodellen behauptet. Dank der Anwenderfreundlichkeit des Verfahrens und der vorhandenen Software ist es auch in der Praxis etabliert.

Die Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM) hat sich in der wirtschafts- und sozialwissenschaftlichen Forschung als geeignetes Verfahren zur Schätzung von Kausalmodellen behauptet. Dank der Anwenderfreundlichkeit des Verfahrens und der vorhandenen Software ist es auch in der Praxis etabliert.

We propose a routine for combining partial least squares-structural equation modeling (PLS-SEM) with selected machine learning (ML) algorithms to exploit the two method’s causal-predictive and causal-exploratory capabilities. Triangulating these two methods can improve the predictive accuracy of research models, enhance the understanding of relationships, assist in identifying new relationships and therewith contribute to theorizing. We demonstrate the advantages and challenges of triangulating the two methods on an illustrative example along a four-step-routine: (1) Develop a PLS-SEM on a baseline conceptual model and use its standards to assess measurement model quality and generate latent variables scores. (2) Apply specific ML algorithms on the extracted data to validate relationships and identify new (linear) relationships that may go beyond the initial hypotheses; similarly, assess model advancements in the form of nonlinearities and interaction effects. (3) Evaluate the theoretical plausibility of alternative models. (4) Integrate alternative models in PLS-SEM and compare these with the baseline model using a recently proposed prediction-oriented test procedure in PLS-SEM.

Die Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM) hat sich in der wirtschafts- und sozialwissenschaftlichen Forschung als geeignetes Verfahren zur Schätzung von Kausalmodellen behauptet. Dank der Anwenderfreundlichkeit des Verfahrens und der vorhandenen Software ist es auch in der Praxis etabliert.

As technology has become indispensable in consumers’ daily life and economic growth, understanding how and why consumers decide to accept and use a new technology has become essential to both academic researchers and practice. This article provides a detailed dataset based on a questionnaire that utilizes an extended technology acceptance model (TAM), incorporating the theory of consumer values and the innovation diffusion theory. Data collection was done with an online survey among French consumers, resulting in a sample size of 174. The dataset contains measures on various consumer attitudes and perceptions (e.g., consumption values) that influence intention and behaviors (adoption intention and technology use). This article supplements a published research article by Richter, Schubring, Hauff, Ringle and Sarstedt [1] which provides a detailed guide on how to combine partial least squares structural equation modeling (PLS-SEM) with necessary condition analysis (NCA) and a related illustration in a standard software published by Richter, Hauff, Ringle, Sarstedt, Kolev and Schubring [2].

Objective: This study examines the individual factors that predict whether individuals will emerge as leaders in global virtual teams, which often lack a more formal leadership structure. Research Design & Methods: We focus on emotional intelligence (EQ) and cultural intelligence (CQ) as two contemporary concepts that are of key relevance to leadership success. Building on socioanalytic theory, we hypothesize that individuals with higher levels of EQ and CQ have a higher probability of emerging as team leaders. We test the hypotheses on a sample of 415 teams comprised of 1 102 individuals who participated in a virtual international collaboration project. Using structural equation modeling, the results reveal that indi-viduals with higher CQ were more likely to emerge as leaders. Findings: Our findings did not support the relevance of EQ. In addition, individual factors such as English pro-ficiency, a higher age, and a lower power distance were also associated with leadership emergence. Implications & Recommendations: The study identified the gap in the literature regarding EQ and CQ in the context of leadership emergence. The results demonstrate that individuals with high CQ and high EQ that may have beneficial effects on the team and its outcomes do not automatically emerge as team lead-ers. We recommend that managers carefully consider which projects and tasks they will leave the leader-ship structure to emerge more informally. Contribution & Value Added: The key contribution and value added of this study is the investigation of the role of CQ and EQ with leadership emergence in global virtual teams (GVT), through the creation of a leader-ship emergence model building on socio-analytic theory.

We provide a comprehensive review of how cross-cultural competence (CCC) has been measured over the past half-century in order to more closely align theoretical constructs and empirical measures. Based on a content analysis of 68 academic and commercial CCC instruments and a supplemental survey of 160 experts, we review the approaches used in these instruments to conceptualize and quantify CCC, discuss their limitations, and recommend best practices and directions for future researchers and practitioners when selecting and using CCC instruments or developing new alternatives.