Purpose - The purpose of this paper is to introduce the importance-performance map analysis (IPMA) and explain how to use it in the context of partial least squares structural equation modeling (PLSSEM). A case study, drawing on the IPMA module implemented in the SmartPLS 3 software, illustrates the results generation and interpretation. Design/methodology/approach - The explications first address the principles of the IPMA and introduce a systematic procedure for its use, followed by a detailed discussion of each step. Finally, a case study on the use of technology shows how to apply the IPMA in empirical PLS-SEM studies. Findings - The IPMA gives researchers the opportunity to enrich their PLS-SEM analysis and, thereby, gain additional results and findings. More specifically, instead of only analyzing the path coefficients (i.e. the importance dimension), the IPMA also considers the average value of the latent variables and their indicators (i.e. performance dimension). Research limitations/implications - An IPMA is tied to certain requirements, which relate to the measurement scales, variable coding, and indicator weights estimates. Moreover, the IPMA presumes linear relationships. This research does not address the computation and interpretation of non-linear dependencies. Practical implications - The IPMA is particularly useful for generating additional findings and conclusions by combining the analysis of the importance and performance dimensions in practical PLS-SEM applications. Thereby, the IPMA allows for prioritizing constructs to improve a certain target construct. Expanding the analysis to the indicator level facilitates identifying the most important areas of specific actions. These results are, for example, particularly important in practical studies identifying the differing impacts that certain construct dimensions have on phenomena such as technology acceptance, corporate reputation, or customer satisfaction. Originality/value - This paper is the first to offer researchers a tutorial and annotated example of an IPMA. Based on a state-of-the-art review of the technique and a detailed explanation of the method, this paper introduces a systematic procedure for running an IPMA. A case study illustrates the analysis, using the SmartPLS 3 software.
Our research aim was to develop a novel clinimetric scale sensitive enough to detect disease progression in primary lateral sclerosis (PLS). A prototype of the PLS Functional Rating Scale (PLSFRS) was generated. Seventy-seven participants with PLS were enrolled and evaluated at 21 sites that comprised the PLSFRS study group. Participants were assessed using the PLSFRS, Neuro-Quality of Life (QoL), Schwab-England Activities of Daily Living (ADL), and the Clinical Global Impression of Change scales. Participants completed telephone assessments at 12, 24, and 48 weeks after enrollment. The PLSFRS demonstrated internal consistency as well as intrarater, interrater, telephone test-retest reliability, and construct validity. Significant changes in disease progression were detected at 6 and 12 months; changes measured by the PLSFRS vs the ALSFRS-R were significantly higher. The PLSFRS is a valid tool to assess the natural history of PLS in a shorter study period.
Purpose – Partial least squares (PLS) path modeling is a variance-based structural equation modeling (SEM) technique that is widely applied in business and social sciences. Its ability to model composites and factors makes it a formidable statistical tool for new technology research. Recent reviews, discussions, and developments have led to substantial changes in the understanding and use of PLS. The paper aims to discuss these issues. Design/methodology/approach – This paper aggregates new insights and offers a fresh look at PLS path modeling. It presents new developments, such as consistent PLS, confirmatory composite analysis, and the heterotrait-monotrait ratio of correlations. Findings – PLS path modeling is the method of choice if a SEM contains both factors and composites. Novel tests of exact fit make a confirmatory use of PLS path modeling possible. Originality/value – This paper provides updated guidelines of how to use PLS and how to report and interpret its results.
Purpose - The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields. Design/methodology/approach - In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, the authors meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage. Findings - PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application. Recent methodological research has extended PLS-SEM's methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity. Research limitations/implications - While research on the PLS-SEM method has gained momentum during the last decade, there are ample research opportunities on subjects such as mediation or multigroup analysis, which warrant further attention. Originality/value - This article provides an introduction to PLS-SEM for researchers that have not yet been exposed to the method. The article is the first to meta-analyze reasons for PLS-SEM usage across the marketing, management, and management information systems fields. The cross-disciplinary review of recent research on the PLS-SEM method also makes this article useful for researchers interested in advanced concepts.
Magnetic porous nanostructures consisting of oriented aggregates of iron oxide nanocrystals display very interesting properties such as a lower oxidation state of magnetite, and enhanced saturation magnetization in comparison with individual nanoparticles of similar sizes and porosity. However, the formation mechanism of these promising nanostructures is not well understood, which hampers the fine tuning of their magnetic properties, for instance by doping them with other elements. Therefore the formation mechanism of porous raspberry shaped nanostructures (RSNs) synthesized by a one-pot polyol solvothermal method has been investigated in detail from the early stages by using a wide panel of characterization techniques, and especially by performing original in situ HR-TEM studies in temperature. A time-resolved study showed the intermediate formation of an amorphous iron alkoxide phase with a plate-like lamellar structure (PLS). Then, the fine investigation of PLS transformation upon heating up to 500 °C confirmed that the synthesis of RSNs involves two iron precursors: the starting one (hydrated iron chlorides) and the in situ formed iron alkoxide precursor which decomposes with time and heating and contributes to the growth step of nanostructures. Such an understanding of the formation mechanism of RSNs is necessary to envision efficient and rational enhancement of their magnetic properties. The formation mechanism of rapsberry-shaped nanostructures with the identification of two iron precursors: the starting one and an intermediate and in situ formed iron alkoxide precursor with a plate-like lamellar structure contributing to the heterogeneous growth step.
Structural equation modeling (SEM) has become a quasi-standard in marketing and management research when it comes to analyzing the cause-effect relations between latent constructs. For most researchers, SEM is equivalent to carrying out covariance-based SEM (CB-SEM). While marketing researchers have a basic understanding of CB-SEM, most of them are only barely familiar with the other useful approach to SEM-partial least squares SEM (PLS-SEM). The current paper reviews PLS-SEM and its algorithm, and provides an overview of when it can be most appropriately applied, indicating its potential and limitations for future research. The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.
Determining fat content in hamburgers is very important to minimize or control the negative effects of fat on human health, effects such as cardiovascular diseases and obesity, which are caused by the high consumption of saturated fatty acids and cholesterol. This study proposed an alternative analytical method based on Near Infrared Spectroscopy (NIR) and Successive Projections Algorithm for interval selection in Partial Least Squares regression ( SPA-PLS) for fat content determination in commercial chicken hamburgers. For this, 70 hamburger samples with a fat content ranging from 14.27 to 32.12 mg kg were prepared based on the upper limit recommended by the Argentinean Food Codex, which is 20% (w w ). NIR spectra were then recorded and then preprocessed by applying different approaches: base line correction, SNV, MSC, and Savitzky-Golay smoothing. For comparison, full-spectrum PLS and the Interval PLS are also used. The best performance for the prediction set was obtained for the first derivative Savitzky-Golay smoothing with a second-order polynomial and window size of 19 points, achieving a coefficient of correlation of 0.94, RMSEP of 1.59 mg kg , REP of 7.69% and RPD of 3.02. The proposed methodology represents an excellent alternative to the conventional Soxhlet extraction method, since waste generation is avoided, yet without the use of either chemical reagents or solvents, which follows the primary principles of Green Chemistry. The new method was successfully applied to chicken hamburger analysis, and the results agreed with those with reference values at a 95% confidence level, making it very attractive for routine analysis.
Methods capable of handling multiple blocks of data have increased in popularity both in industry and academia, namely, in the fields of chemistry, food and beverages, pharmacology, and biology. Among the multiblock methodologies, sequential orthogonalized partial least squares (SO‐PLS) has been attracting great interest, given its interpretation capabilities (eg, the possibility to estimate the relative additional contributions of each block to predict the response and the degree of mutual overlap, ie, the blocks' communalities) associated to desirable modeling features, such as the independence from the relative scaling of the different data blocks and the flexibility to handle blocks with different dimensionalities and pseudo‐ranks. Given the sequential nature of SO‐PLS, it is critically dependent on the order of the blocks used. When a priori knowledge exists about the natural ordering of the blocks (eg, data arising from sequential operations in a production process), this specification is straightforward. However, in the absence of such knowledge or in cases where no order should be preferred a priori (as happens in the case study of this article), SO‐PLS faces the problem of having to find the most adequate one through the exhaustive search of all permutations. This problem is particularly relevant when the number of blocks is larger than 3, getting exponentially worse as this number increases. Situations where the number of natural data blocks is significant are already quite frequent (eg in multistage batch operations) and will tend to occur more often in the future, as Manufacturing 4.0 takes its course and data sources from the entire value chain become more abundant. Therefore, more efficient and systematic approaches are required to retain the benefits of SO‐PLS while coping with the increasing demands for data processing and analysis in Big Data applications. In this article, we introduce Stepwise SO‐PLS as an efficient algorithm for selecting the blocks ordering when performing SO‐PLS, with capabilities of block exclusion (a feature not shared by other current multiblock approaches). A robust statistical comparison framework based on Monte Carlo cross‐validation is employed to compare the proposed stepwise SO‐PLS formulation with the current systematic approach for selecting the block order and its new variant allowing for blocks selection, in their prediction capabilities. A real case study of the prediction of Madeira wine age will be used for establishing the comparison. SO‐PLS is a state of the art multiblock method with interesting prediction and interpretation capabilities. However, this method can become cumbersome when a high number of predictor blocks are available, since its analysis is critically dependent on the order by which the blocks are incorporated in the model. Furthermore, SO‐PLS (as any other current multiblock methods) does not contemplate the possibility for selecting/excluding blocks. In this article, a more efficient approach is presented for establishing the optimal blocks' order in SO‐PLS with capabilities for block selection/exclusion: it is computationally much faster and leads to an optimum or very close to optimum solution regarding the selected blocks and their proper order.