Product Indicator Approach Research Articles

Routines and warmth as protective factors on the relation between housing instability and child outcomes.

Housing instability is associated with numerous poor child outcomes in domains such as behavioral and emotional problems. The impacts of housing instability on child outcomes are typically investigated in the context of sociodemographic risk; however, exploring the role of protective factors (e.g., family routines, parental warmth) in these contexts allows for a more thorough understanding of the effect of housing instability and how potential negative outcomes might be mitigated. The present study further explored the relation between early housing instability and child behavioral and emotional outcomes in middle childhood in addition to the potential moderating role of family routines and parental warmth in early childhood. Longitudinal data of 4,898 families from the Future of Families and Child Wellbeing Study were used to explore these associations. Using structural equation modeling, the present analyses indicated that when controlling for key sociodemographic covariates, housing instability in early childhood was not related to child outcomes at age 9. Greater parental warmth at age 5 was significantly associated with lower levels of behavioral and emotional problems at age 9. Categorical models using the product indicator approach indicated that greater parental warmth attenuates the relation between moving one to two times and later child behavioral problems. Parental warmth emerged as a significant promotive factor for later child emotional problems. Further implications are discussed below. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Resolving the adaptive performance problems in the public sector using purposeful leadership: an empirical investigation in Ghana

PurposePerformance problems in the public sector (PS) necessitate adaptation due to the sector’s uniqueness. Purposeful leadership (PL) may resolve PS adaptive performance (AP) problems. However, there is a paucity of research on the influence of PL on AP. Using data from three large PS organisations in Ghana, this research explores the relationship between PL and AP through job satisfaction (JS) and organisational identification (OI). It also explores the moderating effect of organisational integrity (IN) on the PL–JS and PL–OI relationships.Design/methodology/approachThis paper created and tested a research model using the responses of 875 public servants in Smart PLS 4. Contract fulfilment and perceived organisational support functioned as control factors influencing JS (an intervener). Perceived procedural justice and psychological need satisfaction serve as control factors for OI (another intervener). Age, sex, tenure, education and job position were used as control variables in AP. To assess the role of moderation, we utilised the product indicator approach, and to estimate the role of mediation, we used variance accounted for (VAF). A significance level of 5% was established.FindingsAs anticipated, this study found that PL and AP had a significantly positive connection (t = 2.229, p = 0.000, β = 0.138). Both JS (VAF = 27.37%) and OI (VAF = 39.21%) partially mediated this connection. IN positively moderated the PL–JS (t = 4.249, p = 0.000, β = 0.165) and PL–OI (t = 3.704, p = 0.002, β = 0.099) connections.Research limitations/implicationsThis paper provides a theoretical and empirical understanding of the PL–AP relationship, how this relationship is facilitated and how the PL–JS and PL–OI relationships are strengthened. It provides a roadmap for upcoming scholars to test the hypotheses in diverse PS contexts globally to broaden the field of leadership. It will be insightful to show how JS and OI jointly mediate this relationship and the potential mediating role of job embeddedness in further studies.Practical implicationsHuman resource (HR) practices in PS organisations, such as selecting, leadership enhancement initiatives, promotions, training and performance evaluations, must be guided by a “values-driven strategy” if leadership is to find, cultivate and keep employees capable of devising innovative strategies to manage unforeseen circumstances at work. The leaders are required to demonstrate the values of their organisations in order to set up a role model.Social implicationsThis study highlights the obstacles that purposeful leaders have the potential to mitigate, as well as the prospects that they might offer. It shows the essence for PS organisations to uphold and maintain high integrity standards as their hallmark.Originality/valueThis paper is the first to create and test a research model that shows the relationship between PL and AP. It also shows the different mechanisms (JS and OI) that make this effect possible, as well as the good work environment (i.e. IN) that allows the PL–JS and PL–OI relationships to grow.

The psychology of green behavior: when growth mindset, peer influence and warm glow meet the individual self

PurposeThis study in Iran examined the role of internal and external psychological factors that affected green purchase intention. Moreover, it examined these variables' direct and indirect effects and green purchase intention on green purchase behavior.Design/methodology/approachAn extended version of the theory of planned behavior (TPB) was employed, based on which a theoretical model was designed to reach the authors’ aim. An online questionnaire was used to collect data. For data analysis, confirmatory factor analysis, structural equation modeling, the bootstrapping method and the Preschool Language Scale (PLS) product-indicator approach were conducted to test the proposed conceptual model.FindingsResults show that self-identity, self-interest, self-efficacy and a growth mindset have a positive impact on green purchase intention. However, the study found no predictive effect from peer influence and warm glow. In addition, self-efficacy and green purchase intention significantly affect green purchase behavior. The study reveals that green purchase intention substantially mediates the relationship between self-interest, growth mindset, warm glow and green purchase behavior. Further, warm glow moderates the impact of peer influence, self-identity and self-efficacy on green purchase intention. This study emphasizes the critical role of dispositional factors on green purchase intention and behavior.Originality/valueFew studies consider the effect of the individual self, a growth mindset, a warm glow and peer influence on green purchase intention simultaneously. In addition, the authors introduced a different version of the TPB model. Further, this research also conducted how these variables, directly and indirectly, affect green purchase behavior.

The Relationship between Psychological Factors, Risk Perception and Social Media on Investment Decision Making

The purpose of this paper is to examine the determinants of investor’s investment decision-making in Indian stock markets. The hypothesized direct and indirect effect of psychological factors (anger, positive mood, fear and stress) and risk perception on investment decision making are based on the Behavioural Economic theory. However, the Cultivation theory is used as the basis to test the moderating effect of social media on investment decision-making. The data for the present quantitative study was collected using online survey questionnaire distributed to stock market investors in India. The samples were collected using convenient and snowballing sampling. The research hypotheses were tested and analysed using Partial Least Squares approach for direct and indirect effect test and Product Indicator approach for moderation analysis. Risk perception found to be significant in mediating the relationships between psychological factors such as anger, positive mood and stress towards stock market investment decisions. However, social media does not moderate the relationships between all the tested psychological factors and stock market investment decisions among Indian investors. The findings are beneficial for the stock market investors to be aware of the effect of positive and negative psychological factors on their judgment on risk perception and investment decision making. Despite the claim that social media might strengthen the effect of psychological factors on investment decision-making, this was found to be insignificant in this research.

Openness to experience moderates psychological contract breach–job satisfaction tie-in

Purpose This paper aims to assess the tie-in between psychological contract breach (PCB) and job satisfaction (JST) amongst medical doctors (MDs) working in two stress-prone regions of Ghana, and further analyses the moderating effect of openness to experience (OPE) on this tie-in. Design/methodology/approach Responses from 214 MDs were analysed. Questionnaires were self-administered. Research philosophy was positivism, research approach was quantitative, research design was explanatory and study design was cross-sectional. Test of normality, Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett’s test of sphericity were applied. Both reflective measurement and structural models were assessed. Path coefficients were analysed using partial least squares (PLS) algorithm tool and moderation effect was conducted using the product indicator approach. Control variables were sex (GEN1), age (GEN2), employment type (GEN3) and tenure (GEN1). A significant level was set at 5%. Smart PLS 2.0 M.3 software was used. Findings The analysts found support for a significant moderating effect of OPE on the tie between PCB and JST, such that the consequences of PCB on JST was minimised for MDs who scored high on OPE trait. Practical implications PCB, if not addressed, may lead MDs to be less satisfied with their jobs. In stress-prone health zones where PCB exists, MDs who are inspired, creative, self-sufficient, experimenting and visionary are more likely to be satisfied with their job. Originality/value This study offers health-care literature on the moderating role of OPE personality dimension on the bond between PCB and JST, using PLS-structural equations modelling, which is a superior and robust analytical tool.

The impact of heuristic biases on investors’ investment decision in Pakistan stock market: moderating role of long term orientation

PurposeThe purpose of this paper is to investigate the effect of heuristic biases, namely, availability bias and representativeness bias on investors’ investment decisions in the Pakistan stock exchange, as well as the moderating role of long-term orientation.Design/methodology/approachUsing a structured questionnaire, a total of 374 responses have been collected from individual investors trading in PSX. The relationship was tested by applying the partial least square structural equation model using SmartPLS 3.2.2. Further, Henseler and Chin’s (2010) product indicator approach for moderation analysis was applied to the data set.FindingsThe results revealed that availability bias and representativeness bias have a significant and positive influence on the investment decisions of investors. Furthermore, a significant moderating effect of long term orientation on the effect of representativeness bias on investment decision is observed. This suggests that investors’ long term orientation weaken the effect of representativeness bias on investment decision. However, no significant moderating effect was observed for availability bias.Originality/valueThe paper provides novel insights on the role of heuristic-driven biases on the investment decisions of individual investors in the stock market. Particularly, it enhanced the understanding of behavioral aspects of investment decision-making in an emerging market.

Comparing estimators for latent interaction models under structural and distributional misspecifications.

Estimation methods for structural equation models with interactions of latent variables were compared in several studies. Yet none of these studies examined models that were structurally misspecified. Here, the model-implied instrumental variable 2-stage least square estimator (MIIV-2SLS; Bollen, 1995; Bollen & Paxton, 1998), the 2-stage method of moments estimator (2SMM; Wall & Amemiya, 2003), the nonlinear structural equation mixture model approach (NSEMM; Kelava, Nagengast, & Brandt, 2014), and the unconstrained product indicator approach (UPI; Marsh, Wen, & Hau, 2004) were compared in a Monte Carlo simulation. The design included structural misspecifications in the measurement model involving the scaling indicator or not, the size of the misspecification, normal and nonnormal data, the indicators' reliability, and sample size. For the structural misspecifications that did not involve the scaling indicator, we found that MIIV-2SLS' parameter estimates were less biased compared with 2SMM, NSEMM, and UPI. If the reliability was high, the RMSE for all approaches was very similar; for low reliability, MIIV-2SLS' RMSE became larger compared with the other approaches. If the structural misspecification involved the scaling indicator, all estimators were seriously biased, with the largest bias for MIIV-2SLS. In most scenarios, this bias was more severe for the linear effects than for the interaction effect. The RMSE for conditions with misspecified scaling indicators was smallest for 2SMM, especially for low reliability scenarios, but the overall magnitude of bias was such that we cannot recommend any of the estimators in this situation. Our article showed the damage done when researchers omit cross-loadings of the scaling indicator and the importance of giving more attention to these indicators particularly if the indicators' reliability is low. It also showed that no one estimator is superior to the others across all conditions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Latent Moderation Analysis: A Factor Score Approach

Moderation analysis with latent variables is an important topic in social science research. Although different methods have been proposed for latent moderation analysis in the past three decades, these methods have weaknesses in certain circumstances. We therefore propose the factor score approach as a straightforward and implementation-friendly alternative in latent moderation analysis. This approach has several advantages over other existing methods, such as being able to test higher-order interaction models and interaction-as-outcome models. We compared the empirical performance of the factor score approach and other commonly used methods, namely the unconstrained product indicator approach and the latent moderated structural equation approach, by conducting a simulation study. Results indicated that the factor score approach worked satisfactorily under a range of model conditions. Using these results, we can offer applied researchers some practical guidelines of use for the factor score approach with regard to the subject variable (N/P) ratio and reliability level.

Market orientation, CSR and financial and marketing performance in manufacturing firms in Ghana and Ethiopia

PurposeCorporate social responsibility (CSR) often gives a humanistic touch to the marketing activities of firms and even creates the atmosphere that businesses are reliable. Yet, little is known about its interaction effect on the relationship between market orientation (MO) and financial and marketing performance in emerging economies. The present study aims to comparatively examine the interaction effect of CSR on the direct link between MO and financial and marketing performance in manufacturing firms in Ghana and Ethiopia.Design/methodology/approachThe interaction effect of CSR is examined using a quantitative methodological study design. A total of 439 usable questionnaires across manufacturing firms in Ghana and Ethiopia were collected and analyzed using SEM-PLS 3.0. Analytically, the study used product indicator approach to test the interaction effect of CSR on the nexus between MO and financial and marketing performance, while PLS-multigroup analysis (PLS-MGA) was used to test the significance of the observed differences in the results among the manufacturing firms in the two countries.FindingsResults show that MO significantly improves financial and marketing performance. However, CSR reveals both “suppression” and “spurious” effects on the direct link between MO and financial and marketing performance under varying market conditions. The relationship between MO and financial performance is weakened in Ghanaian manufacturing firms but is strengthened in Ethiopian manufacturing firms when the level of CSR is high. Results, moreover, show that the nexus between MO and marketing performance is strengthened when CSR actions are high in both Ghanaian and Ethiopian manufacturing firms. The PLS-MGA revealed that these differences in findings in the two countries are statistically significant.Practical implicationsThe findings suggest that company managers and marketing practitioners can use CSR in their marketing orientation campaigns to keep high performance and to remain competitive in today’s globalized market.Social implicationsFindings illustrate that incorporation of social interests and sustainability initiatives into firms’ marketing orientation strategies can meet stakeholders’ interest and expectation.Originality/valueThis is one of the few studies that examine comparatively the interaction effect of CSR on the MO–financial and marketing performance linkage in two emerging economies. The study extends our understanding of the RBV and stakeholder theories regarding the role of CSR in firms’ marketing strategies.

An Adaptive Bayesian Lasso Approach with Spike-and-Slab Priors to Identify Multiple Linear and Nonlinear Effects in Structural Equation Models

In applied research, such as with motivation theories, typically many variables are theoretically implied predictors of an outcome and several interactions are assumed (e.g., Watt, 2004). However, estimation problems that might arise when several interaction and/or quadratic effects are analyzed simultaneously have not been investigated because simulation studies on interaction effects in the structural equation modeling framework have mainly focused on small models that contain single interaction effects. In this article, we show that traditional approaches can provide estimates with low accuracy when complex models are estimated. We introduce an adaptive Bayesian lasso approach with spike-and-slab priors that overcomes this problem. Using a complex model in a simulation study, we show that the parameter estimates of the proposed approach are more accurate in situations with high multicollinearity or low reliability compared with a standard Bayesian lasso approach and typical frequentist approaches (i.e., unconstrained product indicator approach and latent moderated structures approach).

Comparison of mobile shopping continuance intention between China and USA from an espoused cultural perspective

Mobile shopping on smartphone platform is becoming popular in many countries. Motivated by research suggesting that behavioral models do not universally hold across cultures, this study investigated mobile shopping continuance intentions under the influence of user espoused cultural values in China and the United States. A research model drawing on UTAUT was developed with perceived effort expectancy, performance expectancy, mobile social influence, and privacy protection as the key determinants. Data from a US sample of 656 and a Chinese sample of 866 were analyzed using Partial Least Square (PLS) procedures to test this model, including the hypothesized antecedent role of some espoused cultural values. The product-indicator approach was adopted to test the hypothesized moderating effect from the espoused cultural values. Contrary to our expectation, the empirical data did not show strong support for the moderating effect of the espoused cultural values. Our data show espoused cultural values more of an antecedent or predictor of mobile shopping continuance. The findings show that culture exerts impact on the mobile shopping continuance decision process both at the macro (country difference) and micro (individual difference as espoused values) level. Relevant discussion, limitations and implications are also provided.

Testing moderating effects in PLS path models with composite variables

Purpose The purpose of this paper is to explain how to model moderating effects of composites using partial least squares (PLS) path modeling. It provides the methodological underpinning of moderating effects in general and describes the various approaches for forming the interaction term, i.e., the product indicator approach, the two-stage approach, and orthogonalization. Design/methodology/approach The paper discusses the use of standardized vs unstandardized construct scores and introduces spotlight analysis as a useful way to report findings. Findings Researchers should rely on unstandardized estimates when analyzing moderating effects. Centering or orthogonalization can help improve the interpretability of path coefficients. Practical implications PLS software implementations should facilitate unstandardized estimates. Originality/value This paper formulates step by step guidelines for analyzing moderating effects of composites using PLS path modeling. It is the first to propose spotlight analysis for PLS path modeling.

A Note on the Specification of Error Structures in Latent Interaction Models.

Latent interaction models have motivated a great deal of methodological research, mainly in the area of estimating such models. Product-indicator methods have been shown to be competitive with other methods of estimation in terms of parameter bias and standard error accuracy, and their continued popularity in empirical studies is due, in part, to their straightforward implementation and relative ease of estimation in mainstream structural equation modeling software. In recent years, the impact of different specifications of the mean structure of the structural model has been the focus of a fair amount of investigation in this area. Yet the effects of misspecification of the error structure of the observed variables implied by the model have not been investigated. In this study, the authors demonstrate algebraically the ramifications of misspecifying these error structures for the unconstrained product-indicator approach. Recommendations to practitioners based on these results are discussed.

Evaluation of model fit in nonlinear multilevel structural equation modeling.

Evaluating model fit in nonlinear multilevel structural equation models (MSEM) presents a challenge as no adequate test statistic is available. Nevertheless, using a product indicator approach a likelihood ratio test for linear models is provided which may also be useful for nonlinear MSEM. The main problem with nonlinear models is that product variables are non-normally distributed. Although robust test statistics have been developed for linear SEM to ensure valid results under the condition of non-normality, they have not yet been investigated for nonlinear MSEM. In a Monte Carlo study, the performance of the robust likelihood ratio test was investigated for models with single-level latent interaction effects using the unconstrained product indicator approach. As overall model fit evaluation has a potential limitation in detecting the lack of fit at a single level even for linear models, level-specific model fit evaluation was also investigated using partially saturated models. Four population models were considered: a model with interaction effects at both levels, an interaction effect at the within-group level, an interaction effect at the between-group level, and a model with no interaction effects at both levels. For these models the number of groups, predictor correlation, and model misspecification was varied. The results indicate that the robust test statistic performed sufficiently well. Advantages of level-specific model fit evaluation for the detection of model misfit are demonstrated.

A comparison of methods for estimating quadratic effects in nonlinear structural equation models.

Two Monte Carlo simulations were performed to compare methods for estimating and testing hypotheses of quadratic effects in latent variable regression models. The methods considered in the current study were (a) a 2-stage moderated regression approach using latent variable scores, (b) an unconstrained product indicator approach, (c) a latent moderated structural equation method, (d) a fully Bayesian approach, and (e) marginal maximum likelihood estimation. Of the 5 estimation methods, it was found that overall the methods based on maximum likelihood estimation and the Bayesian approach performed best in terms of bias, root-mean-square error, standard error ratios, power, and Type I error control, although key differences were observed. Similarities as well as disparities among methods are highlight and general recommendations articulated. As a point of comparison, all 5 approaches were fit to a reparameterized version of the latent quadratic model to educational reading data.

Abstract: Comparing Semiparametric and Parametric Methods for Modeling Interactions Among Latent Variables

Many approaches have been proposed to estimate interactions among latent variables. These methods often assume a specific functional form for the interaction, such as a bilinear interaction. Theory is seldom specific enough to provide a functional form for an interaction, however, so a more exploratory, diagnostic approach may often be required. Bauer (2005) proposed a semiparametric approach that allows for the estimation of interaction effects of unknown functional form among latent variables. A structural equation mixture model (SEMM) is first fit to the data. Then an approximation of the interaction is obtained by aggregating over the mixing components. A simulation study is used to examine the performance of this semiparametric approach to two parametric approaches: the latent moderated structures approach (Klein & Moosbrugger, 2000) and the unconstrained product-indicator approach (Marsh, Wen, & Hau, 2004). Data were generated from four functional forms: main effects only, quadratic trend, bilinear interaction, and exponential interaction. Estimates of bias and root mean squared error of approximation were calculated by comparing the surface used to generate the data and the model-implied surface constructed from each approach. As expected, the parametric approaches were more efficient than the SEMM. For the main effects model, bias was similar for both the SEMM and parametric approaches. For the bilinear interaction, the parametric approaches provided nearly identical results, although the SEMM approach was slightly more biased. When the parametric approaches assumed a bilinear interaction and the data were generated from a quadratic trend or an exponential interaction, the parametric approaches generated biased estimates of the true surface. The SEMM approach approximated the true data generation surface with a similarly low level of bias for all the nonlinear surfaces. For example, Figure 1 shows the true surface for the bilinear interaction along with the SEMM estimated average surface. The results suggest that the SEMM approach can provide a relatively unbiased approximation to variety of nonlinear relationships among latent variables.

Nonlinear structural equation modeling: is partial least squares an alternative?

Nonlinear structural equation modeling provides many advantages over analyses based on manifest variables only. Several approaches for the analysis of latent interaction effects have been developed within the last 15 years, including the partial least squares product indicator approach (PLS-PI), the constrained product indicator approach using the LISREL software (LISREL-PI), and the distribution-analytic latent moderated structural equations approach (LMS) using the Mplus program. An assumed advantage of PLS-PI is that it is able to deal with very large numbers of indicators, while LISREL-PI and LMS have not been investigated under such conditions. In a Monte Carlo study, the performance of LISREL-PI and LMS was compared to PLS-PI results previously reported in Chin et al. (2003) and Goodhue et al. (2007) for identical conditions. The latent interaction model included six indicator variables for the measurement of each latent predictor variable and the latent criterion, and sample size was N=100. The results showed that PLS-PI’s linear and interaction parameter estimates were downward biased, while parameter estimates were unbiased for LISREL-PI and LMS. True standard errors were smallest for PLS-PI, while the power to detect the latent interaction effect was higher for LISREL-PI and LMS. Compared to the symmetric distributions of interaction parameter estimates for LISREL-PI and LMS, PLS-PI showed a distribution that was symmetric for positive values, but included outlying negative estimates. Possible explanations for these findings are discussed.

A Comparison of Approaches for the Analysis of Interaction Effects Between Latent Variables Using Partial Least Squares Path Modeling

In social and business sciences, the importance of the analysis of interaction effects between manifest as well as latent variables steadily increases. Researchers using partial least squares (PLS) to analyze interaction effects between latent variables need an overview of the available approaches as well as their suitability. This article presents 4 PLS-based approaches: a product indicator approach (Chin, Marcolin, & Newsted, 2003), a 2-stage approach (Chin et al., 2003; Henseler & Fassott, in press), a hybrid approach (Wold, 1982), and an orthogonalizing approach (Little, Bovaird, & Widaman, 2006), and contrasts them using data related to a technology acceptance model. By means of a more extensive Monte Carlo experiment, the different approaches are compared in terms of their point estimate accuracy, their statistical power, and their prediction accuracy. Based on the results of the experiment, the use of the orthogonalizing approach is recommendable under most circumstances. Only if the orthogonalizing approach does not find a significant interaction effect, the 2-stage approach should be additionally used for significance test, because it has a higher statistical power. For prediction accuracy, the orthogonalizing and the product indicator approach provide a significantly and substantially more accurate prediction than the other two approaches. Among these two, the orthogonalizing approach should be used in case of small sample size and few indicators per construct. If the sample size or the number of indicators per construct is medium to large, the product indicator approach should be used.

Research Note—Statistical Power in Analyzing Interaction Effects: Questioning the Advantage of PLS with Product Indicators

A significant amount of information systems (IS) research involves hypothesizing and testing for interaction effects. Chin et al. (2003) completed an extensive experiment using Monte Carlo simulation that compared two different techniques for detecting and estimating such interaction effects: partial least squares (PLS) with a product indicator approach versus multiple regression with summated indicators. By varying the number of indicators for each construct and the sample size, they concluded that PLS using product indicators was better (at providing higher and presumably more accurate path estimates) than multiple regression using summated indicators. Although we view the Chin et al. (2003) study as an important step in using Monte Carlo analysis to investigate such issues, we believe their results give a misleading picture of the efficacy of the product indicator approach with PLS. By expanding the scope of the investigation to include statistical power, and by replicating and then extending their work, we reach a different conclusion—that although PLS with the product indicator approach provides higher point estimates of interaction paths, it also produces wider confidence intervals, and thus provides less statistical power than multiple regression. This disadvantage increases with the number of indicators and (up to a point) with sample size. We explore the possibility that these surprising results can be explained by capitalization on chance. Regardless of the explanation, our analysis leads us to recommend that if sample size or statistical significance is a concern, regression or PLS with product of the sums should be used instead of PLS with product indicators for testing interaction effects.