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PurposePartial 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/approachA 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.FindingsThe 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/valueThis 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.

PurposeThis study aims to evaluate the usage of partial least squares structural equation modeling (PLS-SEM) in journals related to logistics and supply chain management (LSCM).Design/methodology/approachBased on a structured literature review approach, the authors reviewed 401 articles in the field of LSCM applying PLS-SEM published in 15 major journals between 2014 and 2022. The analysis focused on reasons for using PLS-SEM, measurement model and structural model evaluation criteria, advanced analysis techniques and reporting practices.FindingsLSCM researchers sometimes did not clarify the reasons for using PLS-SEM, such as sample size, complex models and non-normal distributions. Additionally, most articles exhibit limited use of measurement models and structural model evaluation techniques, leading to inappropriate use of assessment criteria. Furthermore, progress in the practical implementation of advanced analysis techniques is slow, and there is a need for improved transparency in reporting analysis algorithms.Originality/valueThis study contributes to the field of LSCM by providing clear criteria and steps for using PLS-SEM, enriching the understanding and advancement of research methodologies in this field.

PurposeResearchers often use partial least squares structural equation modeling (PLS-SEM) to estimate path models that include formatively specified constructs. Their validation requires running a redundancy analysis, which tests whether the formatively measured construct is highly correlated with an alternative measure of the same construct. Extending prior knowledge in the field, this paper aims to examine the conditions favoring the use of single vs multiple items to measure the criterion construct in redundancy analyses.Design/methodology/approachMerging the literatures from a variety of fields, such as management, marketing and psychometrics, we first provide a theoretical comparison of single-item and multi-item measurement and offer guidelines for designing and validating suitable single items. An empirical comparison in the context of hospitality management examines whether using a single item to measure the criterion variable yields sufficient degrees of convergent validity compared to using a multi-item measure.FindingsThe results of an empirical comparison in the context of hospitality management show that, when the sample size is small, a single item yields higher degrees of convergent validity than a reflective construct does. However, larger sample sizes favor the use of reflectively measured multi-item constructs, but the differences are marginal, thus supporting the use of a global single item in PLS-SEM-based redundancy analyses.Originality/valueThis study is the first to research the efficacy of single-item versus multi-item measures in PLS-SEM-based redundancy analyses. The results illustrate that a convergent validity assessment of formatively measured constructs can be implemented without triggering a pronounced increase in survey length.

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications This paper offers clear guidelines for using PLSpredict, which researchers and practitioners should routinely apply as part of their PLS-SEM analyses. Originality/value 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.

Purpose In China, the practice of livestreaming while shopping has evolved from a form of entertainment into a new business strategy. In recent years, the gastronomy industry has also adopted livestreaming as a means of online promotion. Based on the stimulus–organism–response theoretical model, this study aims to investigate the effects of gastronomy livestreaming on viewers’ impulsive consumption by considering gamification, perceived professionalism and telepresence as causative factors. Design/methodology/approach This study conducted a survey of gastronomy livestreaming viewers that received 1,093 responses. The effects of gamification, perceived professionalism and telepresence were then analyzed using partial least squares-path modeling and necessary condition analysis. Findings This study finds that gamification, perceived professionalism and telepresence are sufficient conditions for explaining impulsive consumption. Innovativeness mediates the relationships between these factors and impulsive consumption. Furthermore, gamification and innovativeness represent necessary conditions for impulsive consumption. Practical implications The findings of this study contribute to an enhanced understanding of livestreaming in the gastronomy industry. Based on these findings, managers in the gastronomy industry can use more interactive gamification activities and enhance telepresence to increase viewers’ impulsive consumption during livestreaming sessions. Originality/value This study identifies the modalities through which gastronomy livestreaming can stimulate impulsive consumption. This is an early study to investigate the effect of experiences of gamification, perceived professionalism and telepresence on viewers’ impulsive consumption in the context of gastronomy livestreaming. In addition, this early study investigates the effect of gastronomy livestreaming innovativeness on impulsive consumption.

PurposeThe purpose of this paper is to assess the effect of two promotional methods, namely, celebrity endorsed advertisement and selfie promotion, on customers’ decision-making processes using the AISAS model.Design/methodology/approachA within-subject experimental design was used to observe how young adults in Malaysia would respond to two promotional methods about a new seafood restaurant. A total of 180 responses were collected using a structured questionnaire. Data were assessed and analysed using partial least squares structural equation modelling.FindingsThe results show that while celebrity endorsed advertisement remains relevant to customer’s decision-making processes, the effect of selfie promotion is comparable to celebrity endorsement. The sequential mediation for both models is found to be significant, but the AISAS model with selfie promotion produces better in-sample prediction (model selection criteria) and out-of-sample prediction (PLSpredict) compared to celebrity endorsed advertisement, thus suggesting its better representation to reality.Research limitations/implicationsDespite being limited to young adults in Malaysia and a particular product, the study is essential to understanding the effect of celebrity endorsed advertisement and selfie promotion on decision-making processes.Practical implicationsThe study provides insights into how business organisations could exploit the advancement of communication technology to encourage selfie behaviour to promote their products in an innovative and competitive manner.Originality/valueThe assessment of the effect of celebrity endorsed advertisement and selfie promotion on decision-making processes using PLSpredict and model selection criteria articulates the relevance of selfie as a promotional tool. It also provides an alternative technique for conducting model comparison research.

Purpose>Since its inception, mobile payment is rapidly gaining popularity over the years, and starting to replace traditional modes of payment. The usage of mobile payments has further escalated following various precautionary measures (i.e. social distancing) in curbing the transmission of the COVID-19 outbreak. However, most of the elderlies are still sceptical about the usage of mobile payment services. The current study was set to investigate the impact of functional, psychological and risk barriers that resulted in elderlies' resistance towards using such services. The impact of stickiness to cash was also examined as a moderator on the investigated relationships.Design/methodology/approach>Online survey questionnaires were used to collect the responses from 400 elderly consumers at the age of 60 and above. Data analysis was then performed using the SPSS and AMOS statistical software packages.Findings>Findings obtained acknowledged the significance of functional (i.e. perceived complexity, perceived incompatibility and perceived cost), psychological (i.e. lack of trust, inertia, and technological anxiety) and risk (i.e. privacy risk, security risk, financial risk and operational risk) barriers in influencing resistance towards mobile payment services among the elderlies. Consequently, resistance would influence their attitude and non-adoption intention; with attitude as the mediator between resistance and non-adoption intention. Finally, moderation analysis also confirmed the moderating effect of stickiness to cash towards elevating the correlation between resistance and non-adoption intention.Originality/value>This study is one of the very few studies that explored the minimally investigated territory on the consequential importance of mobile payment usage among the elderlies, specifically, through extending the literature on the impact of functional, psychological and risk barriers towards the individuals' resistance. Besides, this study also successfully contributed to existing body of knowledge by highlighting the mediating role of attitude and moderating role of stickiness to cash in the interrelationships between resistance, attitude and non-adoption intention.

PurposeThe increasing popularity of live-streaming commerce has provided a new opportunity for e-retailers to boost sales. This study integrated signaling theory and social exchange theory to investigate how streamer- and product-centered signals influence customers’ likelihood of making an impulsive purchase in the live-streaming commerce context.Design/methodology/approachAn online survey was designed and distributed to the target respondents in China using purposive sampling. A total of 735 valid responses were analyzed with partial least square structural equation modeling (PLS-SEM).FindingsBoth streamer-centered signals, i.e. streamer credibility and streamer interaction quality, were discovered to significantly influence product-centered signal, i.e. product information quality. Additionally, streamer interaction quality was found to have a significant impact on streamer credibility. Furthermore, it was observed that customer engagement played a significant mediating role in the relationship between product information quality and impulsive buying tendency. Moreover, the paths between product information quality and customer engagement, as well as the connection between engagement and impulsive buying tendency, were found to be moderated by guanxi orientation.Originality/valueDespite the prevalence of impulsive purchases in live-streaming commerce, few studies have empirically investigated the impact of streamer and product signals on influencing customers’ impulsive purchase decisions. Consequently, to the best of our knowledge, this study distinguishes itself by offering empirical insights into how streamers use reciprocating relationship mechanisms to communicate signals that facilitate impulsive purchase decisions.

PurposeThe corporate atmosphere in recent times speaks volumes about the crises of confidence and credibility brewing among professionals due to the rising incidences of unethical pro-organizational behavior (UPB). The study developed a model to demonstrate the underlying mechanisms through which unethical organizational culture (UOC) influences UPB through the mediating roles of idealism and relativism.Design/methodology/approachUsing a cross-sectional approach, data were collected through questionnaires that were distributed to small and medium-sized enterprises (SMEs) operating in the Plateau state in Nigeria. A total of 269 responses were obtained and analyzed using the partial least squares structural equation modeling (PLS-SEM) technique via Smart-PLS software.FindingsThe results revealed that the relationship between UOC and UPB was significant. The indirect predictive role of UOC on UPB was established via relativism but not through idealism. The results indicate that the preponderance of UPB among SMEs is a product of UOC which breeds a relativist ideology that ultimately promotes UPB. Finally, implications and suggestions for further research are discussed.Originality/valueThis study contributes to UPB in two unique ways. First, the authors bring to the fore the critical role of UOC in the debate on UPB which has been under-explored. Second, the study also established the mediating role of relativism in the relationship between UOC and UPB.

Live streaming e-commerce (LSE) platforms are information systems that blend real-time entertainment with instant purchasing to offer novel experiences to users. Drawing on cognitive appraisal theory, this study explains how LSE experiences stimulate users’ emotions and impulse buying behavior through a sequential process consisting of primary appraisal, secondary appraisal, and behavioral outcomes. Using a sample drawn from millennial users (18-35 years old), we conducted two studies. In the initial pilot investigation (n = 425), we examined users’ perceptions and behaviors when using LSE platforms; in the main study (n = 200), we explored the potential mediating and moderating factors affecting impulse buying tendencies. Overall, the findings show that LSE affordances predict the zero moment of truth (ZMOT) value on LSE platforms. The results also indicate that both trust in the streamer and emotional engagement play a mediating role in the relationship between ZMOT value and impulse buying tendencies. Finally, we found that trust in the LSE platform plays an important moderating role in strengthening the prediction of impulse buying tendencies. This study concludes with recommendations for future LSE research and practice.