Purpose: Partial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria.Design/methodology/approach: A systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field.Findings: The study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM.Originality/value: This research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.

This study examines the factors that influence individual intentions towards the adoption of full electric vehicles. A sample including 308 respondents was collected on the streets of Macau. The collected data were analysed by confirmatory factor analysis and structural equation modelling. The results demonstrate that environmental concerns and the perception of environmental policy are antecedent factors of the perception of full electric vehicles, which influences the behavioural intention to purchase full electric vehicles. This study also finds that the perception of economic benefit is one of the key factors influencing the adoption of full electric vehicles. Vehicle operators seek economic benefits from future long-term fuel savings, high energy efficiency, and cheap electricity. Thus, a government striving to promote low-carbon transportation needs to scale up its efforts to enhance citizens’ environmental concerns and to establish proper environmental policy as well as to provide long-term financial and strategic support for electric vehicles.

“Awesome” is one of the most highly desirable experiences for tourists. This study investigates how tourists’ awe emotion is induced when tourists visit sacred mountains and how the awe experience influences their satisfaction. A survey is administrated at a famous sacred mountain in China—Mount Emei. Results reveal that the awe experience is more elicited by the perceived vastness of natural environment for secular tourists, while is more encouraged by the perceived sanctity of religious ambience for pilgrim tourists. Awe experience is a mediator between the sense of perceptual vastness/sanctity and tourists’ satisfaction. The mediation relationships through awe experience are moderated by the visitor types (pilgrims and secular tourists). Findings suggest that destination marketers should apply tourism strategies to encourage tourists’ sense of awe.

With the rapid growth of the sharing economy, access-based services have emerged as an alternative and/or complementary to traditional ownership-based services. The access-based services are enabled by means of Smart Product-Service Systems (SPSSs) that integrate smart products and e-services into a single solution. However, there is a lack of studies that cover the acceptance factors for both smart products and e-services of SPSSs. Therefore, it is important to have a study to explore the factors that influence the acceptance of SPSSs. This study develops a conceptual framework which consists of the perceived interactivity of mobile apps and the particularity of the smart shared products as antecedents apart from perceived usefulness and perceived ease of use, as suggested by the Technology Acceptance Model. To test the research framework empirically, a self-reported online survey was conducted among bike sharing program users in China. A total of 520 valid responses were collected, and the partial least-square structural equation modeling (PLS-SEM) technique was used to examine the research model. The empirical results suggest that the perceived interactivity of mobile apps and the particularity of smart shared products are two significant sets of antecedents that influence consumers’ perceived ease of use and perceived usefulness, and the perceived ease of use and perceived usefulness are preconditions for the acceptance of SPSSs. The findings generate practical suggestions for SPSSs providers to increase the network size of users, improve the interactivity of mobile apps, and manage the distributions of service points.