UCSI University

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.

The metaverse has the potential to extend the physical world using augmented and virtual reality technologies allowing users to seamlessly interact within real and simulated environments using avatars and holograms. Virtual environments and immersive games (such as, Second Life, Fortnite, Roblox and VRChat) have been described as antecedents of the metaverse and offer some insight to the potential socio-economic impact of a fully functional persistent cross platform metaverse. Separating the hype and “meta…” rebranding from current reality is difficult, as “big tech” paints a picture of the transformative nature of the metaverse and how it will positively impact people in their work, leisure, and social interaction. The potential impact on the way we conduct business, interact with brands and others, and develop shared experiences is likely to be transformational as the distinct lines between physical and digital are likely to be somewhat blurred from current perceptions. However, although the technology and infrastructure does not yet exist to allow the development of new immersive virtual worlds at scale - one that our avatars could transcend across platforms, researchers are increasingly examining the transformative impact of the metaverse. Impacted sectors include marketing, education, healthcare as well as societal effects relating to social interaction factors from widespread adoption, and issues relating to trust, privacy, bias, disinformation, application of law as well as psychological aspects linked to addiction and impact on vulnerable people. This study examines these topics in detail by combining the informed narrative and multi-perspective approach from experts with varied disciplinary backgrounds on many aspects of the metaverse and its transformational impact. The paper concludes by proposing a future research agenda that is valuable for researchers, professionals and policy makers alike.

Determining an appropriate sample size is vital in drawing realistic conclusions from research findings. Although there are several widely adopted rules of thumb to calculate sample size, researchers remain unclear about which one to consider when determining sample size in their respective studies. ‘How large should the sample be?’ is one the most frequently asked questions in survey research. The objective of this editorial is three-fold. First, we discuss the factors that influence sample size decisions. Second, we review existing rules of thumb related to the calculation of sample size. Third, we present the guidelines to perform power analysis using the G*Power programme. There is, however, a caveat: we urge researchers not to blindly follow these rules. Such rules or guidelines should be understood in their specific contexts and under the conditions in which they were prescribed. We hope that this editorial does not only provide researchers a fundamental understanding of sample size and its associated issues, but also facilitates their consideration of sample size determination in their own studies.

Partial least squares structural equation modeling (PLS-SEM) has become a standard tool for analyzing complex inter-relationships between observed and latent variables in tourism and numerous other fields of scientific inquiry. Along with the recent surge in the method’s use, research has contributed several complementary methods for assessing the robustness of PLS-SEM results. Although these improvements are documented in extant literature, research on tourism has been slow to adopt the relevant complementary methods. This article illustrates the use of recent advances in PLS-SEM, designed to ensure structural model results’ robustness in terms of nonlinear effects, endogeneity, and unobserved heterogeneity in a PLS-SEM framework. Our overarching aim is to encourage the routine use of these complementary methods to increase methodological rigor in the field.

BACKGROUND: More than 80% of deaths from cardiovascular disease are estimated to occur in low-income and middle-income countries, but the reasons are unknown. METHODS: We enrolled 156,424 persons from 628 urban and rural communities in 17 countries (3 high-income, 10 middle-income, and 4 low-income countries) and assessed their cardiovascular risk using the INTERHEART Risk Score, a validated score for quantifying risk-factor burden without the use of laboratory testing (with higher scores indicating greater risk-factor burden). Participants were followed for incident cardiovascular disease and death for a mean of 4.1 years. RESULTS: The mean INTERHEART Risk Score was highest in high-income countries, intermediate in middle-income countries, and lowest in low-income countries (P<0.001). However, the rates of major cardiovascular events (death from cardiovascular causes, myocardial infarction, stroke, or heart failure) were lower in high-income countries than in middle- and low-income countries (3.99 events per 1000 person-years vs. 5.38 and 6.43 events per 1000 person-years, respectively; P<0.001). Case fatality rates were also lowest in high-income countries (6.5%, 15.9%, and 17.3% in high-, middle-, and low-income countries, respectively; P=0.01). Urban communities had a higher risk-factor burden than rural communities but lower rates of cardiovascular events (4.83 vs. 6.25 events per 1000 person-years, P<0.001) and case fatality rates (13.52% vs. 17.25%, P<0.001). The use of preventive medications and revascularization procedures was significantly more common in high-income countries than in middle- or low-income countries (P<0.001). CONCLUSIONS: Although the risk-factor burden was lowest in low-income countries, the rates of major cardiovascular disease and death were substantially higher in low-income countries than in high-income countries. The high burden of risk factors in high-income countries may have been mitigated by better control of risk factors and more frequent use of proven pharmacologic therapies and revascularization. (Funded by the Population Health Research Institute and others.).

BACKGROUND: The optimal range of sodium intake for cardiovascular health is controversial. METHODS: We obtained morning fasting urine samples from 101,945 persons in 17 countries and estimated 24-hour sodium and potassium excretion (used as a surrogate for intake). We examined the association between estimated urinary sodium and potassium excretion and the composite outcome of death and major cardiovascular events. RESULTS: The mean estimated sodium and potassium excretion was 4.93 g per day and 2.12 g per day, respectively. With a mean follow-up of 3.7 years, the composite outcome occurred in 3317 participants (3.3%). As compared with an estimated sodium excretion of 4.00 to 5.99 g per day (reference range), a higher estimated sodium excretion (≥ 7.00 g per day) was associated with an increased risk of the composite outcome (odds ratio, 1.15; 95% confidence interval [CI], 1.02 to 1.30), as well as increased risks of death and major cardiovascular events considered separately. The association between a high estimated sodium excretion and the composite outcome was strongest among participants with hypertension (P=0.02 for interaction), with an increased risk at an estimated sodium excretion of 6.00 g or more per day. As compared with the reference range, an estimated sodium excretion that was below 3.00 g per day was also associated with an increased risk of the composite outcome (odds ratio, 1.27; 95% CI, 1.12 to 1.44). As compared with an estimated potassium excretion that was less than 1.50 g per day, higher potassium excretion was associated with a reduced risk of the composite outcome. CONCLUSIONS: In this study in which sodium intake was estimated on the basis of measured urinary excretion, an estimated sodium intake between 3 g per day and 6 g per day was associated with a lower risk of death and cardiovascular events than was either a higher or lower estimated level of intake. As compared with an estimated potassium excretion that was less than 1.50 g per day, higher potassium excretion was associated with a lower risk of death and cardiovascular events. (Funded by the Population Health Research Institute and others.).

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.

In a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

BACKGROUND: Socioeconomic status is associated with differences in risk factors for cardiovascular disease incidence and outcomes, including mortality. However, it is unclear whether the associations between cardiovascular disease and common measures of socioeconomic status-wealth and education-differ among high-income, middle-income, and low-income countries, and, if so, why these differences exist. We explored the association between education and household wealth and cardiovascular disease and mortality to assess which marker is the stronger predictor of outcomes, and examined whether any differences in cardiovascular disease by socioeconomic status parallel differences in risk factor levels or differences in management. METHODS: In this large-scale prospective cohort study, we recruited adults aged between 35 years and 70 years from 367 urban and 302 rural communities in 20 countries. We collected data on families and households in two questionnaires, and data on cardiovascular risk factors in a third questionnaire, which was supplemented with physical examination. We assessed socioeconomic status using education and a household wealth index. Education was categorised as no or primary school education only, secondary school education, or higher education, defined as completion of trade school, college, or university. Household wealth, calculated at the household level and with household data, was defined by an index on the basis of ownership of assets and housing characteristics. Primary outcomes were major cardiovascular disease (a composite of cardiovascular deaths, strokes, myocardial infarction, and heart failure), cardiovascular mortality, and all-cause mortality. Information on specific events was obtained from participants or their family. FINDINGS: <0·0001). Medical care (eg, management of hypertension, diabetes, and secondary prevention) seemed to play an important part in adverse cardiovascular disease outcomes because such care is likely to be poorer in people with the lowest levels of education compared to those with higher levels of education in low-income countries; however, we observed less marked differences in care based on level of education in middle-income countries and no or minor differences in high-income countries. INTERPRETATION: Although people with a lower level of education in low-income and middle-income countries have higher incidence of and mortality from cardiovascular disease, they have better overall risk factor profiles. However, these individuals have markedly poorer health care. Policies to reduce health inequities globally must include strategies to overcome barriers to care, especially for those with lower levels of education. FUNDING: Full funding sources are listed at the end of the paper (see Acknowledgments).

The advancement of the World Wide Web has resulted in the creation of a new form of retail transactions- electronic retailing (e-tailing) or web-shopping. Thus, customers’ involvements in online purchasing have become an important trend. As such, it is vital to identify the determinants of the customer online purchase intention. The aim of this research is to evaluate the impacts of shopping orientations, online trust and prior online purchase experience to the customer online purchase intention. A total of 242 undergraduate information technology students from a private university in Malaysia participated in this research. The findings revealed that impulse purchase intention, quality orientation, brand orientation, online trust and prior online purchase experience were positively related to the customer online purchase intention.

The term metaverse is described as the next iteration of the Internet. Metaverse is a virtual platform that uses extended reality technologies, i.e. augmented reality, virtual reality, mixed reality, 3D graphics, and other emerging technologies to allow real-time interactions and experiences in ways that are not possible in the physical world. Companies have begun to notice the impact of the metaverse and how it may help maximize profits. The purpose of this paper is to offer perspectives on several important areas, i.e. marketing, tourism, manufacturing, operations management, education, the retailing industry, banking services, healthcare, and human resource management that are likely to be impacted by the adoption and use of a metaverse. Each includes an overview, opportunities, challenges, and a potential research agenda.

Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin’s theory of evolution and has been studied extensively to solve different areas of optimisation and engineering applications since its introduction by Storn in 1997. This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years. The methodology used to search for relevant DE papers and an overview of the original DE are firstly explained. Recent advances in the modifications proposed to enhance the effectiveness and efficiency of the original DE are reviewed by analysing the strengths and weaknesses of each published work, followed by the potential applications of these DE variants in solving different real-world engineering problems. In contrast to most existing DE review papers, additional analyses are performed in this survey by investigating the impacts of various parameter settings on given DE variants to identify their optimal values required for solving certain problem classes. The qualities of modifications incorporated into selected DE variants are also evaluated by measuring the performance gains achieved in terms of search accuracy and/or efficiency against the original DE. The additional surveys conducted in this study are anticipated to provide more insightful perspectives for both beginners and experts of DE research, enabling their better understanding about current research trends and new motivations to outline appropriate strategic planning for future development works.

The richness of high-value bio-compounds derived from microalgae has made microalgae a promising and sustainable source of useful product. The present work starts with a review on the usage of open pond and photobioreactor in culturing various microalgae strains, followed by an in-depth evaluation on the common harvesting techniques used to collect microalgae from culture medium. The harvesting methods discussed include filtration, centrifugation, flocculation, and flotation. Additionally, the advanced extraction technologies using ionic liquids as extractive solvents applied to extract high-value bio-compounds such as lipids, carbohydrates, proteins, and other bioactive compounds from microalgae biomass are summarized and discussed. However, more work needs to be done to fully utilize the potential of microalgae biomass for the application in large-scale production of biofuels, food additives, and nutritive supplements.

In recent years, increasing interest has been shown in targeting energy efficiency as a roadmap for carbon mitigation, limiting energy use, improving buildings’ energy performance, and reducing energy consumption for achieving sustainable buildings. This article presents a systematic review to provide the best practices in this area and identify the challenges, motivations, recommendations, and pathways for future work. Discussing the methodological aspects gives insights for future researchers. This research used papers published on three scientific and reliable databases—Web of Science, ScienceDirect, and IEEE Xplore-from 2014 to May 23, 2021. The selected papers reached N = 134 based on inclusion and exclusion criteria and divided into review papers, proceeding conference, and articles. The review articles (N = 16/134) give an overall view on improving energy efficiency to achieve sustainability in buildings by using green building rating systems, developing and implementing policies, technology utilization, adopting techniques, and applying strategies. The conferences (N = 33/134) and articles (N = 85/134) focus more on details of different aspects of improving energy efficiency by reducing environmental, economic, social, and other impacts. A few articles proposed multiple-criteria decision-making methods to solve energy efficiency gaps for promoting sustainability in buildings. Achieving energy efficiency toward sustainable buildings is a hot topic in the sustainable development area. The outcomes from this paper will provide a valuable reference to stakeholders, governments, and decision-makers and give suggestions from the selected past studies. This review will provide motivation and attract future research endeavors in the field.

Scholars believe that the newly introduced Industry 5.0 has the potential to move beyond the profit-centered productivity of Industry 4.0 and to promote sustainable development goals such as human-centricity, socio-environmental sustainability, and resilience. However, little has been done to understand how this ill-defined phenomenon may deliver its indented sustainability values despite these speculative promises. To address this knowledge gap, the present study developed a strategy roadmap that explains the mechanism by which Industry 5.0 delivers its intended sustainable development functions. The study first developed and introduced the Industry 5.0 reference model that describes the technical and functional properties of this phenomenon. The study further conducted a content-centric synthesis of the literature and identified the sustainable development functions of Industry 5.0. Next, the interpretive structural modeling (ISM) technique was employed to identify the sequential relationships among the functions and construct the Industry 5.0-enabled model of sustainable development. The ISM involved collecting the opinions of 11 Industry 5.0 experts through expert panel meetings. Results revealed that Industry 5.0 delivers sustainable development values through 16 functions. Circular intelligent products, employee technical assistance, intelligent automation, open sustainable innovation, renewable integration, and supply chain adaptability are examples of the functions identified. These functions are highly interrelated and should be developed in a specific order so that the synergies and complementarities among them would maximize the sustainable development value gains. The roadmap to Industry 5.0-driven sustainability developed in this study is expected to provide a better understanding of ways Industry 5.0 can contribute to sustainable development, explaining how the development of its functions should be managed to maximize their synergies and contribution to the intended sustainability values. The study also highlights important avenues for future research, emphasizing the potential enablers of Industry 5.0 development, such as Government 5.0 or Corporate Governance 5.0.

Purpose: This study objective was to explore the pattern of self-medications among King Khalid University students, Saudi Arabia. Patients and methods: A cross-sectional study was conducted over five months among King Khalid University students, Abha, Saudi Arabia. Results: Among all the study participants, nearly 98.7% were practicing self-medication. Headache (75.9%), cough and cold (52.5%), and fever (35.6%) and body pain (24.6%) were the most reported symptoms. Use of painkillers (91.6%) was significantly predominant among the medical students, whereas non-medical students used antibiotics (35.4%).Time saving (64.2%), mild symptom (51.7%) and quick relief (36.9%) were the reasons behind seeking self-medication in this study. Conclusion: Self-medications was common in King Khalid University. Educational programs are highly recommended. Keywords: self-medication, students, medical, nonmedical, King Khalid University, Saudi Arabia

The rapid growth of technologies not only formulates life easier but also exposes a lot of security issues. With the advancement of the Internet over years, the number of attacks over the Internet has been increased. Intrusion Detection System (IDS) is one of the supportive layers applicable to information security. IDS provide a salubrious environment for business and keeps away from suspicious network activities. Recently, Machine Learning (ML) algorithms are applied in IDS in order to identify and classify the security threats. This paper explores the comparative study of various ML algorithms used in IDS for several applications such as fog computing, Internet of Things (IoT), big data, smart city, and 5G network. In addition, this work also aims for classifying the intrusions using ML algorithms like Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART) and Random Forest. The work was tested with the KDD-CUP dataset and their efficiency was measured and also compared along with the latest researches.

Partial least squares structural equation modeling (PLS-SEM) is one of the most widely used methods of multivariate data analysis. Although previous research has discussed different aspects of PLS-SEM, little has been done to explain the attributes of the various PLS-SEM statistical applications. The objective of this editorial is to discuss the multiple PLS-SEM applications, including SmartPLS, WarpPLS, and ADANCO. It is written based on information received from the developers via emails as well as our ongoing understanding and experience of using these applications. We hope this editorial will serve as a manual for users to understand the unique characteristics of each PLS-SEM application and make informed decisions on the most appropriate application for their research.

Lactococcus lactis has progressed a long way since its discovery and initial use in dairy product fermentation, to its present biotechnological applications in genetic engineering for the production of various recombinant proteins and metabolites that transcends the heterologous species barrier. Key desirable features of this gram-positive lactic acid non-colonizing gut bacteria include its generally recognized as safe (GRAS) status, probiotic properties, the absence of inclusion bodies and endotoxins, surface display and extracellular secretion technology, and a diverse selection of cloning and inducible expression vectors. This have made L. lactis a desirable and promising host on par with other well established model bacterial or yeast systems such as Escherichia coli, Saccharomyces [corrected] cerevisiae and Bacillus subtilis. In this article, we review recent technological advancements, challenges, future prospects and current diversified examples on the use of L. lactis as a microbial cell factory. Additionally, we will also highlight latest medical-based applications involving whole-cell L. lactis as a live delivery vector for the administration of therapeutics against both communicable and non-communicable diseases.

BACKGROUND: Most data on mortality and prognostic factors in patients with heart failure come from North America and Europe, with little information from other regions. Here, in the International Congestive Heart Failure (INTER-CHF) study, we aimed to measure mortality at 1 year in patients with heart failure in Africa, China, India, the Middle East, southeast Asia and South America; we also explored demographic, clinical, and socioeconomic variables associated with mortality. METHODS: We enrolled consecutive patients with heart failure (3695 [66%] clinic outpatients, 2105 [34%] hospital in patients) from 108 centres in six geographical regions. We recorded baseline demographic and clinical characteristics and followed up patients at 6 months and 1 year from enrolment to record symptoms, medications, and outcomes. Time to death was studied with Cox proportional hazards models adjusted for demographic and clinical variables, medications, socioeconomic variables, and region. We used the explained risk statistic to calculate the relative contribution of each level of adjustment to the risk of death. FINDINGS: We enrolled 5823 patients within 1 year (with 98% follow-up). Overall mortality was 16·5%: highest in Africa (34%) and India (23%), intermediate in southeast Asia (15%), and lowest in China (7%), South America (9%), and the Middle East (9%). Regional differences persisted after multivariable adjustment. Independent predictors of mortality included cardiac variables (New York Heart Association Functional Class III or IV, previous admission for heart failure, and valve disease) and non-cardiac variables (body-mass index, chronic kidney disease, and chronic obstructive pulmonary disease). 46% of mortality risk was explained by multivariable modelling with these variables; however, the remainder was unexplained. INTERPRETATION: Marked regional differences in mortality in patients with heart failure persisted after multivariable adjustment for cardiac and non-cardiac factors. Therefore, variations in mortality between regions could be the result of health-care infrastructure, quality and access, or environmental and genetic factors. Further studies in large, global cohorts are needed. FUNDING: The study was supported by Novartis.