University of South Alabama

Purpose The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power and the need for goodness-of-fit testing. Next, the metrics as well as the rules of thumb that should be applied to assess the PLS-SEM results are covered. Besides presenting established PLS-SEM evaluation criteria, the overview includes the following new guidelines: PLSpredict (i.e., a novel approach for assessing a model’s out-of-sample prediction), metrics for model comparisons, and several complementary methods for checking the results’ robustness. Design/methodology/approach This paper provides an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLS-SEM. Findings Most of the previously applied metrics for evaluating PLS-SEM results are still relevant. Nevertheless, scholars need to be knowledgeable about recently proposed metrics (e.g. model comparison criteria) and methods (e.g. endogeneity assessment, latent class analysis and PLSpredict), and when and how to apply them to extend their analyses. Research limitations/implications Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but must always be up to date with the latest developments in the PLS-SEM method. Originality/value In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions.

The last decade has seen a sharp increase in the number of scientific publications describing physiological and pathological functions of extracellular vesicles (EVs), a collective term covering various subtypes of cell-released, membranous structures, called exosomes, microvesicles, microparticles, ectosomes, oncosomes, apoptotic bodies, and many other names. However, specific issues arise when working with these entities, whose size and amount often make them difficult to obtain as relatively pure preparations, and to characterize properly. The International Society for Extracellular Vesicles (ISEV) proposed Minimal Information for Studies of Extracellular Vesicles ("MISEV") guidelines for the field in 2014. We now update these "MISEV2014" guidelines based on evolution of the collective knowledge in the last four years. An important point to consider is that ascribing a specific function to EVs in general, or to subtypes of EVs, requires reporting of specific information beyond mere description of function in a crude, potentially contaminated, and heterogeneous preparation. For example, claims that exosomes are endowed with exquisite and specific activities remain difficult to support experimentally, given our still limited knowledge of their specific molecular machineries of biogenesis and release, as compared with other biophysically similar EVs. The MISEV2018 guidelines include tables and outlines of suggested protocols and steps to follow to document specific EV-associated functional activities. Finally, a checklist is provided with summaries of key points.

The purposes of this article are to position mixed methods research ( mixed research is a synonym) as the natural complement to traditional qualitative and quantitative research, to present pragmatism as offering an attractive philosophical partner for mixed methods research, and to provide a framework for designing and conducting mixed methods research. In doing this, we briefly review the paradigm “wars” and incompatibility thesis, we show some commonalities between quantitative and qualitative research, we explain the tenets of pragmatism, we explain the fundamental principle of mixed research and how to apply it, we provide specific sets of designs for the two major types of mixed methods research ( mixed-model designs and mixed-method designs), and, finally, we explain mixed methods research as following (recursively) an eight-step process. A key feature of mixed methods research is its methodological pluralism or eclecticism, which frequently results in superior research (compared to monomethod research). Mixed methods research will be successful as more investigators study and help advance its concepts and as they regularly practice it.

The purpose of this article is to examine how the field of mixed methods currently is being defined. The authors asked many of the current leaders in mixed methods research how they define mixed methods research. The authors provide the leaders' definitions and discuss the content found as they searched for the criteria of demarcation. The authors provide a current answer to the question, What is mixed methods research? They also briefly summarize the recent history of mixed methods and list several issues that need additional work as the field continues to advance. They argue that mixed methods research is one of the three major “research paradigms” (quantitative research, qualitative research, and mixed methods research). The authors hope this article will contribute to the ongoing dialogue about how mixed methods research is defined and conceptualized by its practitioners.

It is now clear that oxygen-derived free radicals play an important part in several models of experimentally induced reperfusion injury. Although there are certainly multiple components to clinical ischemic and reperfusion injury, it appears likely that free-radical production may make a major contribution at certain stages in the progression of the injury. The primary source of superoxide in reperfused reoxygenated tissues appears to be the enzyme xanthine oxidase, released during ischemia by a calcium-triggered proteolytic attack on xanthine dehydrogenase. Reperfused tissues are protected in a variety of laboratory models by scavengers of superoxide radicals or hydroxyl radicals or by allopurinol or other inhibitors of xanthine oxidase. Dysfunction induced by free radicals may thus be a major component of ischemic diseases of the heart, bowel, liver, kidney, and brain.

The reduction of oxygen to water proceeds via one electron at a time. In the mitochondrial respiratory chain, Complex IV (cytochrome oxidase) retains all partially reduced intermediates until full reduction is achieved. Other redox centres in the electron transport chain, however, may leak electrons to oxygen, partially reducing this molecule to superoxide anion (O(2)(−)•). Even though O(2)(−)• is not a strong oxidant, it is a precursor of most other reactive oxygen species, and it also becomes involved in the propagation of oxidative chain reactions. Despite the presence of various antioxidant defences, the mitochondrion appears to be the main intracellular source of these oxidants. This review describes the main mitochondrial sources of reactive species and the antioxidant defences that evolved to prevent oxidative damage in all the mitochondrial compartments. We also discuss various physiological and pathological scenarios resulting from an increased steady state concentration of mitochondrial oxidants.

The reduction of oxygen to water proceeds via one electron at a time. In the mitochondrial respiratory chain, Complex IV (cytochrome oxidase) retains all partially reduced intermediates until full reduction is achieved. Other redox centres in the electron transport chain, however, may leak electrons to oxygen, partially reducing this molecule to superoxide anion (O2-*). Even though O2-* is not a strong oxidant, it is a precursor of most other reactive oxygen species, and it also becomes involved in the propagation of oxidative chain reactions. Despite the presence of various antioxidant defences, the mitochondrion appears to be the main intracellular source of these oxidants. This review describes the main mitochondrial sources of reactive species and the antioxidant defences that evolved to prevent oxidative damage in all the mitochondrial compartments. We also discuss various physiological and pathological scenarios resulting from an increased steady state concentration of mitochondrial oxidants.

Numerous statistical methods are available for social researchers. Therefore, knowing the appropriate technique can be a challenge. For example, when considering structural equation modelling (SEM), selecting between covariance-based (CB-SEM) and variance-based partial least squares (PLS-SEM) can be challenging. This paper applies the same theoretical measurement and structural models and dataset to conduct a direct comparison. The findings reveal that when using CB-SEM, many indicators are removed to achieve acceptable goodness-of-fit, when compared to PLS-SEM. Also, composite reliability and convergent validity were typically higher using PLS-SEM, but other metrics such as discriminant validity and beta coefficients are comparable. Finally, when comparing variance explained in the dependent variable indicators, PLS-SEM was substantially better than CB-SEM. Updated guidelines assist researchers in determining whether CB-SEM or PLS-SEM is the most appropriate method to use.

Coastal ecosystems and the services they provide are adversely affected by a wide variety of human activities. In particular, seagrass meadows are negatively affected by impacts accruing from the billion or more people who live within 50 km of them. Seagrass meadows provide important ecosystem services, including an estimated $1.9 trillion per year in the form of nutrient cycling; an order of magnitude enhancement of coral reef fish productivity; a habitat for thousands of fish, bird, and invertebrate species; and a major food source for endangered dugong, manatee, and green turtle. Although individual impacts from coastal development, degraded water quality, and climate change have been documented, there has been no quantitative global assessment of seagrass loss until now. Our comprehensive global assessment of 215 studies found that seagrasses have been disappearing at a rate of 110 km(2) yr(-1) since 1980 and that 29% of the known areal extent has disappeared since seagrass areas were initially recorded in 1879. Furthermore, rates of decline have accelerated from a median of 0.9% yr(-1) before 1940 to 7% yr(-1) since 1990. Seagrass loss rates are comparable to those reported for mangroves, coral reefs, and tropical rainforests and place seagrass meadows among the most threatened ecosystems on earth.

Purpose Following the call for awareness of accepted reporting practices by Ringle, Sarstedt, and Straub in 2012, the purpose of this paper is to review and analyze the use of partial least squares structural equation modeling (PLS-SEM) in Industrial Management & Data Systems ( IMDS ) and extend MIS Quarterly ( MISQ ) applications to include the period 2012-2014. Design/methodology/approach Review of PLS-SEM applications in information systems (IS) studies published in IMDS and MISQ for the period 2010-2014 identifying a total of 57 articles reporting the use of or commenting on PLS-SEM. Findings The results indicate an increased maturity of the IS field in using PLS-SEM for model complexity and formative measures and not just small sample sizes and non-normal data. Research limitations/implications Findings demonstrate the continued use and acceptance of PLS-SEM as an accepted research method within IS. PLS-SEM is discussed as the preferred SEM method when the research objective is prediction. Practical implications This update on PLS-SEM use and recent developments will help authors to better understand and apply the method. Researchers are encouraged to engage in complete reporting procedures. Originality/value Applications of PLS-SEM for exploratory research and theory development are increasing. IS scholars should continue to exercise sound practice by reporting reasons for using PLS-SEM and recognizing its wider applicability for research. Recommended reporting guidelines following Ringle et al. (2012) and Gefen et al. (2011) are included. Several important methodological updates are included as well.

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.

Higher-order constructs, which facilitate modeling a construct on a more abstract higher-level dimension and its more concrete lower-order subdimensions, have become an increasingly visible trend in applications of partial least squares structural equation modeling (PLS-SEM). Unfortunately, researchers frequently confuse the specification, estimation, and validation of higher-order constructs, for example, when it comes to assessing their reliability and validity. Addressing this concern, this paper explains how to evaluate the results of higher-order constructs in PLS-SEM using the repeated indicators and the two-stage approaches, which feature prominently in applied social sciences research. Focusing on the reflective-reflective and reflective-formative types of higher-order constructs, we use the well-known corporate reputation model example to illustrate their specification, estimation, and validation. Thereby, we provide the guidance that scholars, marketing researchers, and practitioners need when using higher-order constructs in their studies.

Reaction of 1-butyl imidazole with 3-bromopropylamine hydrobromide, followed by workup and anion exchange, yields a new room temperature ionic liquid incorporating a cation with an appended amine group. The new ionic liquid reacts reversibly with CO2, reversibly sequestering the gas as a carbamate salt. The new ionic liquid, which can be repeatedly recycled in this role, is comparable in efficiency for CO2 capture to commercial amine sequestering reagents, and yet is nonvolatile and does not require water to function.

Discussions concerning different structural equation modeling methods draw on an increasing array of concepts and related terminology. As a consequence, misconceptions about the meaning of terms such as reflective measurement and common factor models as well as formative measurement and composite models have emerged. By distinguishing conceptual variables and their measurement model operationalization from the estimation perspective, we disentangle the confusion between the terminologies and develop a unifying framework. Results from a simulation study substantiate our conceptual considerations, highlighting the biases that occur when using (1) composite-based partial least squares path modeling to estimate common factor models, and (2) common factor-based covariance-based structural equation modeling to estimate composite models. The results show that the use of PLS is preferable, particularly when it is unknown whether the data's nature is common factor- or composite-based.

This article provides researchers with knowledge of how to design a high quality mixed methods research study. To design a mixed study, researchers must understand and carefully consider each of the dimensions of mixed methods design, and always keep an eye on the issue of validity. We explain the seven major design dimensions: purpose, theoretical drive, timing (simultaneity and dependency), point of integration, typological versus interactive design approaches, planned versus emergent design, and design complexity. There also are multiple secondary dimensions that need to be considered during the design process. We explain ten secondary dimensions of design to be considered for each research study. We also provide two case studies showing how the mixed designs were constructed.

Ionic liquids offer a unique suite of properties that make them important candidates for a number of energy related applications. Cation–anion combinations that exhibit low volatility coupled with high electrochemical and thermal stability, as well as ionic conductivity, create the possibility of designing ideal electrolytes for batteries, super-capacitors, actuators, dye sensitised solar cells and thermo-electrochemical cells. In the field of water splitting to produce hydrogen they have been used to synthesize some of the best performing water oxidation catalysts and some members of the protic ionic liquid family co-catalyse an unusual, very high energy efficiency water oxidation process. As fuel cell electrolytes, the high proton conductivity of some of the protic ionic liquid family offers the potential of fuel cells operating in the optimum temperature region above 100 °C. Beyond electrochemical applications, the low vapour pressure of these liquids, along with their ability to offer tuneable functionality, also makes them ideal as CO2 absorbents for post-combustion CO2 capture. Similarly, the tuneable phase properties of the many members of this large family of salts are also allowing the creation of phase-change thermal energy storage materials having melting points tuned to the application. This perspective article provides an overview of these developing energy related applications of ionic liquids and offers some thoughts on the emerging challenges and opportunities.

Purpose Partial least squares structural equation modeling (PLS-SEM) is an important statistical technique in the toolbox of methods that researchers in marketing and other social sciences disciplines frequently use in their empirical analyses. The purpose of this paper is to shed light on several misconceptions that have emerged as a result of the proposed “new guidelines” for PLS-SEM. The authors discuss various aspects related to current debates on when or when not to use PLS-SEM, and which model evaluation metrics to apply. In addition, this paper summarizes several important methodological extensions of PLS-SEM researchers can use to improve the quality of their analyses, results and findings. Design/methodology/approach The paper merges literature from various disciplines, including marketing, strategic management, information systems, accounting and statistics, to present a state-of-the-art review of PLS-SEM. Based on these findings, the paper offers a point of orientation on how to consider and apply these latest developments when executing or assessing PLS-SEM-based research. Findings This paper offers guidance regarding situations that favor the use of PLS-SEM and discusses the need to consider certain model evaluation metrics. It also summarizes how to deal with endogeneity in PLS-SEM, and critically comments on the recent proposal to adjust PLS-SEM estimates to mimic common factor models that are the foundation of covariance-based SEM. Finally, this paper opposes characterizing common concepts and practices of PLS-SEM as “out-of-date” without providing well-substantiated alternatives and solutions. Research limitations/implications The paper paves the way for future discussions and suggests a way forward to reach consensus regarding situations that favor PLS-SEM use and its application. Practical implications This paper offers guidance on how to consider the latest methodological developments when executing or assessing PLS-SEM-based research. Originality/value This paper complements recently proposed “new guidelines” with the aim of offering a counter perspective on some strong claims made in the latest literature on PLS-SEM. It also clarifies some misconceptions regarding the application of PLS-SEM.

Numerous statistical methods are available for social researchers. Therefore, knowing the appropriate technique can be a challenge. For example, when considering structural equation modelling (SEM), selecting between covariance-based (CB-SEM) and variance-based partial least squares (PLS-SEM) can be challenging. This paper applies the same theoretical measurement and structural models and dataset to conduct a direct comparison. The findings reveal that when using CB-SEM, many indicators are removed to achieve acceptable goodness-of-fit, when compared to PLS-SEM. Also, composite reliability and convergent validity were typically higher using PLS-SEM, but other metrics such as discriminant validity and beta coefficients are comparable. Finally, when comparing variance explained in the dependent variable indicators, PLS-SEM was substantially better than CB-SEM. Updated guidelines assist researchers in determining whether CB-SEM or PLS-SEM is the most appropriate method to use.

A 14-item, self-administered, multidimensional, functional social support questionnaire was designed and evaluated on 401 patients attending a family medicine clinic. Patients were selected from randomized time-frame sampling blocks during regular office hours. The population was predominantly white, female, married, and under age 45. Eleven items remained after test-retest reliability was assessed over a 1- to 4-week follow-up period. Factor analysis and item remainder analysis reduced the remaining 11 items to a brief and easy-to-complete two-scale, eight-item functional social support instrument. Construct validity, concurrent validity, and discriminant validity are demonstrated for the two scales (confidant support--five items and affective support--three items). Factor analysis and correlations with other measures of social support suggest that the three remaining items (visits, instrumental support, and praise) are distinct entities that may need further study.

This article reports on a large‐scale (n = 1,056), exploratory factor analysis study that determined the underlying constructs that comprise student barriers to online learning. The eight factors found were (a) administrative issues, (b) social interaction, (c) academic skills, (d) technical skills, (e) learner motivation, (f) time and support for studies, (g) cost and access to the Internet, and (h) technical problems. Independent variables that significantly affected student ratings of these barrier factors included: gender, age, ethnicity, type of learning institution, self‐rating of online learning skills, effectiveness of learning online, online learning enjoyment, prejudicial treatment in traditional classes, and the number of online courses completed.