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This study aims to propose guidelines for the joint use of partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to combine symmetric and asymmetric perspectives in model evaluation, in the hospitality and tourism field.

This study discusses PLS-SEM as a symmetric approach and fsQCA as an asymmetric approach to analyze structural and configurational models. It presents guidelines to conduct an fsQCA based on latent construct scores drawn from PLS-SEM, to assess how configurations of exogenous constructs produce a specific outcome in an endogenous construct.

This research highlights the advantages of combining PLS-SEM and fsQCA to analyze the causal effects of antecedents (i.e., exogenous constructs) on outcomes (i.e., endogenous constructs). The construct scores extracted from the PLS-SEM analysis of a nomological network of constructs provide accurate input for performing fsQCA to identify the sufficient configurations required to predict the outcome(s). Complementing the assessment of the model’s explanatory and predictive power, the fsQCA generates more fine-grained insights into variable relationships, thereby offering the means to reach better managerial conclusions.

The application of PLS-SEM and fsQCA as separate prediction-oriented methods has increased notably in recent years. However, in the absence of clear guidelines, studies applied the methods inconsistently, giving researchers little direction on how to best apply PLS-SEM and fsQCA in tandem. To address this concern, this study provides guidelines for the joint use of PLS-SEM and fsQCA.

The purpose of this paper is to build on recent efforts occurring within business-to-business (B2B) marketing research to advance methodological developments. As phenomena within B2B relationships have become increasingly complex, marketing scholars have begun to point out the limitations associated with correlation-based methodological approaches and highlight the need for new developments in this area. One such development is the fuzzy set qualitative comparative analysis (fsQCA).

This study uses a literature review to examine the use of fsQCA in B2B marketing research.

First, the current manuscript presents the benefits that the application of fsQCA can offer to market researchers investigating B2B phenomena. Second, the paper presents the current state of fsQCA use within B2B marketing. Third, it suggests possible marketing B2B research topics that can be explored using fsQCA.

The study highlights the benefits of fsQCA, presents the current state of fsQCA use within B2B marketing and offers a rich future research agenda for B2B marketing scholars. This agenda can also help spur additional method developments in the discipline.

The purpose of this paper is to explore the configuration of factors affecting the accuracy of triage decision-making. The contribution of the work is twofold: first, it develops a protocol for applying a fuzzy-set qualitative comparative analysis (fsQCA) in the context of triage decision-making, and second, it studies, through two pilot cases, the interplay between individual and organizational factors in determining the emergence of errors in different decisional situations.

The methodology adopted in this paper is the qualitative comparative analysis (QCA). The fuzzy-set variant of QCA (fsQCA) is implemented. The data set has been collected during field research carried out in the Emergency Departments (EDs) of two Italian public hospitals.

The results of this study show that the interplay between individual and contextual/organizational factors determines the emergence of errors in triage assessment. Furthermore, there are some regularities in the patterns discovered in each of the investigated organizational contexts. These findings suggest that we should avoid isolating individual factors from the context in which nurses make their decisions.

Previous research on triage has mainly explored the impact of homogeneous groups of factors on the accuracy of the triage process, without considering the complexity of the phenomenon under investigation. This study outlines the need to consider the not-linear relationships among different factors in the study of triage’s decision-making. The definition and implementation of a protocol to apply fsQCA to the triage process in EDs further contributes to the originality of the research.

Given the complexity of building information modeling (BIM) adoption decisions in small- and medium-sized enterprises (SMEs) in the Architecture, Engineering and Construction (AEC) industry, understanding BIM adoption decision-making through the net effect of a single factor on BIM adoption decisions alone is limited. Therefore, this paper analyzed the co-movement effect of managers' psychological factors on the BIM adoption decisions from the perspective of managers' perceptions. The purpose is to let managers have a deep understanding of their BIM adoption decisions, and put forward targeted suggestions for the AEC industry to promote the adoption of BIM by SMEs.

Data from 192 managers in SMEs collected by the questionnaire were used in a fuzzy set qualitative comparative analysis (fsQCA). Due to the limitations of fsQCA in making the best use of the data used, as a complement to fsQCA, necessary conditions analysis (NCA) was used to analyze the extent to which necessary conditions influenced the outcome.

(1) NCA analysis shows that high perceived resource availability (PRA) and high performance expectancy (PE) are necessary conditions for high BIM adoption intention (AI). (2) fsQCA analysis shows that high PE is the single core condition for high AI. fsQCA analysis identifies three configurations of managers' psychological factors, reflecting three types of managers' decision preferences, namely benefit preference, loss aversion and risk avoidance, respectively. Different decision preferences may lead to different BIM adoption strategies, such as full in-house use, partial in-house/outsourcing and full outsourcing of BIM processes. (3) High perceived risk (PR) and low perceived business value of BIM (PBV) are the core conditions for low AI.

This paper expands on the application of fsQCA to context of BIM adoption decisions. Based on the results of fsQCA analysis, this paper also establishes the relationship between managers' decision-making psychology and BIM adoption strategy choice and analyzes the impact of different decision biases on BIM adoption strategy choice. It concludes with suggestions for encouraging managers to adopt BIM and for avoiding decision-making bias.

The purpose of this paper is to revisit the conceptualization and measurement of entrepreneurial intentions. Significant studies anchored in the Theory of Planned Behavior use causal statistical approaches to entrepreneurial intentions. This methodological approach, leads to the conclusion that there is a single pathway for all groups of people to achieve business start-up. Even though theory suggests approaches by women entrepreneurs to start a business may be influenced by different factors from those influencing men, results are inconclusive in these analyses. The authors argue that methodological preferences for linear, causal analytical approaches limit the understanding of gender similarities and differences in the business start-up process. The authors propose that when considering diverse samples, it is unreasonable to assume there is only a single pathway leading to business start-up.

Building on fuzzy-set Qualitative Comparative Analysis (fsQCA) and data set of 2,038 respondents, the authors investigate factors predicting the intentions to start a business and evaluate the alternative conjunctive paths that emerge.

The fsQCA results shows that the relationship among conditions leading to entrepreneurial intentions is complex and is best represented as multiple and conjectural causation configurations. In other words, there are multiple significant pathways (refers to equifinality) that predict intentions to start a business start-up, and there are significant differences by gender.

This study is one of the first to examine the roll of gender as a sperate condition in the analysis. This paper offers implications for theory and future research and highlights the complexity of this domain.

The diffusion of electronic commerce has a notable impact on the economy's prosperity. This paper embraces complexity theory principles to examine the factors affecting Internet users' acceptance and use of electronic retailers. It is essential for the sustainability of electronic retailers to understand the motivations impacting online consumer behaviour. Symmetrical and asymmetrical methods are combined to examine the relationship between perceived ease of use, perceived enjoyment, web characteristics, online consumer reviews (OCRs) and online purchase intention. Further, symmetry and differences between males and females were examined.

Data collected from 425 online consumers using an online structured survey was analysed using structural equation modelling (SEM) and fuzzy set qualitative comparative analysis (fsQCA). The net effects and causal configurations of the four proposed variables and online purchase intention were examined.

The SEM findings confirmed the significance of perceived enjoyment, website characteristics and OCRs on online purchase intention. Perceived enjoyment mediated the relationship between perceived ease of use and online purchase intention. The multi-group analysis confirmed the difference in antecedent impacts between males and females. The fsQCA findings revealed that multiple recipes lead to the occurrence of online purchase intention; in addition, the recipes leading to its absence do not mirror the previous ones.

The present study embraces complexity theory concepts in understanding online purchase intention using fsQCA methodology; further, the role of gender in online consumer behaviour was highlighted in the result discussion.

Drawing upon the importance of research and development (R&D) alliances in driving firm innovation performance, extant research has analyzed individually the impact of R&D alliance partner attributes on firm innovation performance. Despite such analyzes, research has generally underestimated the configurations of partner attributes leading to firm innovation performance. This research gap is interesting to explore, as firms involved in R&D alliances usually face a combination of partner attributes. Moreover, gaining a better understanding of how R&D partner attributes tie into configurations is an issue that is attracting particular interest in coopetition research and alliance literature. This paper aims to obtain a better knowledge of this underrated, but important, aspect of alliances by exploring what configurations of R&D alliance partner attributes lead firms involved in R&D alliances to achieve high innovation performance. To tackle this question, first, this study reviews the extant literature on R&D alliances by relying on the knowledge-based view of alliances to identify the most impactful partner attributes on firms’ innovation performance. This paper then applies a fuzzy set qualitative comparative analysis (fsQCA) to explore the configurations of R&D alliance partner attributes that lead firms involved in R&D alliances to achieve high innovation performance.

This study selects 27 R&D alliances formed worldwide in the telecom industry. This paper explores the multiple configurations of partner attributes of these alliances by conducting a fsQCA.

The findings of the fsQCA show that the two alternate configurations of partner attributes guided the firms involved in these alliances to achieve high innovation performance: a configuration with extensive partner technological relatedness and coopetition, but no experience; and a configuration with extensive partner experience and competition, but no technological relatedness.

The research highlights the importance of how partner attributes (i.e. partner technological relatedness, partner competitive overlap, partner experience and partner relative size) tie, with regard to the firms’ access to external knowledge and consequently to their willingness to achieve high innovation performance. Moreover, this paper reveals the beneficial effect of competition on the innovation performance of the firms involved in R&D alliances when some of the other knowledge-based partner attributes are considered. Despite these insights for alliance and coopetition literature, some limitations are to be noted. First, some of the partners’ attributes considered could be further disentangled into sub-partner attributes. Second, other indicators might be used to measure firms’ innovation performance. Third, as anticipated this study applies fsQCA to explore the combinatory effects of partner attributes in the specific context of R&D alliances in the telecom industry worldwide and in a specific time window. This condition may question the extensibility of the results to other industries and times.

This study also bears two interesting implications for alliance managers. First, the paper suggests that R&D alliance managers need to be aware that potential alliance partners have multiple attributes leading to firm innovation performance. In this regard, partner competitive overlap is particularly important for gaining a better understanding of firm innovation performance. When looking for strategic partners, managers should try to ally with highly competitive enterprises so as to access their more innovative knowledge. Second, the results also highlight that this beneficial effect of coopetition in R&D alliances can be amplified in two ways. On the one hand, when the partners involved in the alliance have not yet developed experience in forming alliances. Partners without previous experience supply ideal stimuli to unlock more knowledge in the alliance because new approaches to access and develop knowledge in the alliance could be explored. On the other hand, this paper detects the situation when the allied partners are developing technologies and products in different areas. When partnering with firms coming from different technological areas, the knowledge diversity that can be leveraged in the alliances could drive alliance managers to generate synergies and economies of scope within the coopetitive alliance.

Extant research has analyzed individually the impact of R&D alliance partner attributes on firm innovation performance but has concurrently underestimated the configurations of partner attributes leading to firm innovation performance. Therefore, this paper differs from previous studies, as it provides an understanding of the specific configurations of R&D alliance partner attributes leading firms involved in R&D alliances to achieve high innovation performance.

The authors aim to obtain the optimal combinations of factors from institutional environment adaptation mechanisms and internal resources or capabilities that influence the sustainability of a firm.

The authors develop a new index, called the sustainability index, based on the stakeholder perspective by employing a corporate credit risk index, an evaluation of a firm's corporate governance, corporate financial performance and firm age. The authors then apply both Ordinary Least Squares (OLS) Regression Analysis and Fuzzy set Qualitative Comparative Analysis (FsQCA) to obtain the optimal models for firms' sustainability.

The OLS analysis shows that the variables including financial leverage, slack, innovation capability, manufacturing capability and human capital that have significant influences on the sustainability of firms. Our FsQCA analysis obtains configurations of several solutions for firm sustainability and concludes that the fit of combinations of institutional factors and/or internal resources and capabilities of a firm is related to its sustainability.

The limitations in our new index include these: first, one may add more key metrics to measure the index; second, the findings do not provide any necessary nor a sufficient condition to get sustainability for sure. The limitations of using multiple regression analysis are that it is not able to reveal the combinations of causal conditions that can lead to the outcome in the real world as well as to the sustainability of a firm in our study. To overcome the limitations, the authors apply fsQCA analysis to identify combinations of causal conditions to a firm's sustainability in our study.

Introducing the sustainability index enables us to find out all factors influencing the sustainability of a firm. The authors’ analysis can be used to identify combinations of causal conditions to lead to outcomes in the real world. Their analysis enables managers to know how to predict the sustainability of the firm. For example, the authors’ fsQCA analysis shows that low marketing capability will lead to the high sustainability of the firm. This information helps managers to make the decision or plan to achieve good results toward their businesses and get better allocate their resources and get a better investment.

The authors’ analysis can be used to identify combinations of causal conditions to lead to outcomes in the real world and enable managers to know how to predict the sustainability of the firm. A correct prediction can assist companies in developing their future operations, which would enhance their competitiveness vis-à-vis rivals during this time of global economic volatility, which, in turn, enables firms to perform better and employ more employees that could help the entire society.

The sustainability index the authors developed in our paper is new in the literature and the findings obtained by both OLS Regression Analysis and FsQCA are new in predicting a firm's sustainability. The authors’ findings are useful for academics, managers and policymakers in predicting and maintaining a firm's sustainability.

Organizations nowadays require services supplied by shared service centers (SSCs) to achieve organizational responsiveness. Previous contributions focused on distinct qualitative-explorative factors for explaining successful SSC implementation but failed to consider the interdependencies and combined effects between factors.

Drawing on complexity and configuration theories, this research employed a fuzzy-set qualitative comparative analysis (fsQCA). A unique dataset of 121 international firms was obtained to examine the combined effects of five conditions (factors), namely, modularization, standardization, decision-rights, portfolio and customer-orientation .

The findings show that multiple configurations of conditions (or solutions) can lead to successful SSC implementation. The fsQCA results indicated that portfolio and standardization are perceived as core conditions in all configurations. Firms that focus on portfolio and continuous evaluation of customer-orientation are more likely to be successful. Furthermore, in some configurations, the size of the firm size matters.

The cross-sectional survey data might be a potential limitation. In future research, a more extensive survey can be collected to help generalize the results.

Success factors are dependent on the SSC configuration. Standardization, portfolio management and regular evaluations of changing customer services by executive management are needed.

To the best of the authors' knowledge, there is no academic study that examines SSC implementation based on salient conditions using a configurational thinking approach. As such, the findings of the research allow us to better understand the causal complexity and interdependencies between essential SSC factors.

In the complex environments of online personalization, multiple factors have been considered to explain consumers’ online behaviour, but largely without considering the role of specific configurations of variables and how they may affect consumer behaviour. This study aims to show how trust towards online vendors, privacy, emotions and experience combine to predict consumers’ purchase intentions.

Building on complexity theory, a conceptual model followed by research propositions is presented. The propositions are empirically validated through configurational analysis, using fuzzy-set qualitative comparative analysis (fsQCA) on 182 customers with experience in personalized online shopping. Predictive validity analysis is also performed.

Five solutions of trust, privacy, emotions and experience increase intention to purchase, and six solutions inhibit it. The findings verify the importance of trust and happiness in successful personalized online shopping. Their absence inhibits purchase intentions. Also, high experience may help to overcome low trust or negative emotions, whereas low experience requires the combination of high trust and happiness. None of the examined factors are indispensable to explain purchase intentions.

The study uses fsQCA, differentiating from traditional studies in the area that use variance-based methods and identifies multiple solutions explaining the same outcome. The proposed approach contributes to theory development in the field.

The multiple solutions lead to new ways on how companies may approach their customers, as each one covers a specific part of the sample, adding to the fact that in personalized marketing there is not one single optimal solution explaining customer purchase intentions.

This study contributes by extending existing knowledge on how trust, privacy, emotions and experience combine to increase or mitigate intention to purchase towards the development of new emotion-centric theories and the design and provision of personalized services and presenting a step-by-step methodological approach for how to apply fsQCA in e-commerce studies.