Partial least squares path modeling using ordinal categorical indicators

Run! This road has no ending! A systematic review of PLS-SEM application in strategic management research among developing nations

Partial least squares structural equation modelling (PLS-SEM) is emerging as a prominent methodological tool in strategic management research. Although it offers various advancements to stay relevant with growing research needs, the pace of PLS-SEM adoption may differ in different parts of the world. In this paper, we conducted a systematic review using the PRISMA framework and extracted from the top-ranking strategic management journals 120 articles published between 2011 and 2022 that presented a microscopic view on developing nations. Our findings reveal that despite the astounding methodological solutions offered by PLS-SEM, the studies from developing nations are still trailing behind developed nations in terms of fully exploiting the advancements of PLS-SEM to provide substantial insights to strategic management literature. This review identifies discrepancies in the current application of the method, discusses the most recent advancements and provides the best practices, standard guidelines and recommendations for the best use of PLS-SEM in strategic management research.

Analysis of the influence of the COVID-19 outbreak on household solid waste management: An empirical study using PLS-SEM

The global increase in solid waste pollution due to economic growth and population growth has led to severe ecological deterioration. This situation is exacerbated by the ongoing coronavirus disease (COVID-19) pandemic, which has significantly impacted consumer behavior and consumption habits. Proper management of solid waste has thus become critical in mitigating environmental degradation. In this study, we aim to examine the relationship between the outbreak of COVID-19, environmental attitudes (EATT), social norms (SN), environmental knowledge (EK), technological innovation (TI), and solid waste management (SWM) in Ecuadorian households during the pandemic. We collected data from 426 households in Ecuador using an online questionnaire and analyzed it using the Partial Least Squares Structural Equation Model method to test the hypotheses. Likewise, we validated the measurement and structural model. The results are novel about SWM, considering a developing country severely affected during the pandemic. Our findings suggest that COVID-19 has a negative impact on SWM, while EATT, SN, and TI have a positive influence on SWM. However, we found that EK does not significantly affect SWM.

A perspective on using partial least squares structural equation modelling in data articles

This perspective article on using partial least squares structural equation modelling (PLS-SEM) is intended as a guide for authors who wish to publish datasets that can be analysed with this method as stand-alone data articles. Stand-alone data articles are different from supporting data articles in that they are not linked to a full research article published in another journal. Nevertheless, authors of stand-alone data articles will be required to clearly demonstrate and justify the usefulness of their dataset. This perspective article offers actionable recommendations regarding the conceptualisation phase, the types of data suitable for PLS-SEM and quality criteria to report, which are generally applicable to studies using PLS-SEM. We also present adjusted versions of the HTMT metric for discriminant validity testing that broaden its applicability. Further, we highlight the benefit of linking data articles to already published research papers that employ the PLS-SEM method.

Characterization and prediction of gambling behavior in adolescents using the COM-B model

Gambling is an international phenomenon, posing a serious threat to adolescents who begin gambling at a young age. This study aims, to explore gambling behavior in adolescents and interpret its risk factors. We conducted a three-waves cohort longitudinal study assessing gambling and associated risk factors in south-eastern Spain. Data were analyzed using the Capabilities, Opportunities, Motivations, Behavior (COM-B) model and the partial least squares path modelling (PLS-PM) technique. Gambling was measured by frequency and money spent; associated factors were knowledge about gambling, parental attitude towards gambling, risk perception, normative perception, and intention to gamble. These items were assigned as indicators of each construct of the COM-B model-capability, opportunity, motivation, and behavior-using the theoretical domains framework. Once the behavior was performed, feedback on future capability, opportunity, and motivation was observed. Results show that capability, determined by past experience, and opportunity, determined by parental attitudes, motivates adolescents to seek gambling experiences in the future. Identifying such factors that affect gambling behavior in adolescents and establishing relationships between them through a robust theoretical model is essential for designing effective interventions.

Envelope-based partial partial least squares with application to cytokine-based biomarker analysis for COVID-19

Partial least squares (PLS) regression is a popular alternative to ordinary least squares regression because of its superior prediction performance demonstrated in many cases. In various contemporary applications, the predictors include both continuous and categorical variables. A common practice in PLS regression is to treat the categorical variable as continuous. However, studies find that this practice may lead to biased estimates and invalid inferences (Schuberth et al., 2018). Based on a connection between the envelope model and PLS, we develop an envelope-based partial PLS estimator that considers the PLS regression on the conditional distributions of the response(s) and continuous predictors on the categorical predictors. Root-n consistency and asymptotic normality are established for this estimator. Numerical study shows that this approach can achieve more efficiency gains in estimation and produce better predictions. The method is applied for the identification of cytokine-based biomarkers for COVID-19 patients, which reveals the association between the cytokine-based biomarkers and patients' clinical information including disease status at admission and demographical characteristics. The efficient estimation leads to a clear scientific interpretation of the results.

Revealing Differences in Brand Loyalty and Brand Engagement of Single or no Parented Young Adults

The present study reveals differences in brand engagement, brand loyalty and brand evangelism among single or no parented young adults. It was found that persons who were raised without one or both parents for at least three years show higher emotional connectivity to brands compared to their counterparts from a control group. A widely recognized structural equation modelling technique—partial least squares method—has been applied in order to test the research hypotheses. Two Baltic States, namely, Lithuania and Latvia, served as an empirical basis of the study. The results of the research are relevant not only to business related disciplines, such as marketing and management, but also to other social sciences fields presuming the existence of more psychology-related differences among single or no parented persons.

Multigroup Analysis in Information Systems Research using PLS-PM

Heterogeneity is a pertinent issue in Information Systems (IS) research because human behavior often differs across groups. In the partial least squares path modeling (PLS-PM) context, several approaches have been proposed to investigate potential group differences. Despite the availability of numerous approaches, literature that compares their efficacy is sparse. Consequently, IS researchers lack guidance on which approach is best suited to detect group differences. We address this issue by presenting the results of an extensive Monte Carlo simulation study that juxtaposes the various approaches' behavior under numerous conditions. In doing so, we first provide an overview on existing approaches proposed for multigroup analysis (MGA) in the PLS-PM context. Moreover, we derive important implications for applied research: Firstly, we show that the omnibus test of group differences (OTG) and approaches based on the comparison of confidence intervals are not recommendable for MGA. Secondly, we provide detailed information as to which approaches are suitable for comparing one specific path coefficient and which are recommended if the complete structural model is compared across groups. Finally, we show that approaches which are designed to compare a single parameter require an adjustment for multiple comparisons when used to compare more than two groups.

Examining predictors of digital library use: an application of the information system success model

Purpose

Digital libraries are not only an assortment of information assets yet have turned into a digital community for correspondence, searching and electronic learning. Also, economically the investment in terms of money, time, energy and manpower associated with the development of effective digital library (DL) systems demands high utilization of these resources. This study aims to explore the factors that affect the utilization of digital libraries and may lead to users’ satisfaction and finally high exposure to information systems like digital libraries. However, these factors may work differently in different cultures. Considering this fact, DeLone and McLean’s IS success model (ISSM) is tested and expanded in a local academic context.

Design/methodology/approach

Following the quantitative research design, a total of 355 responses were collected through a questionnaire-based survey. Research scholars of the University of Punjab, Lahore, Pakistan were the reviewed population of this study. A two-stage stratified random sampling method was used to choose the sample. Structural equation modelling is used to find out the nature and extent of the relationship among studied variables.

Findings

The findings confirm that service quality is the strong predictor of DL system use, whereas overall users’ satisfaction mediates the relationship between the predictors (content and service quality) and the outcome variable (use).

Originality/value

This work done is the first main endeavour to use the “Information System Success” theories to intervene and mediate the effect of content, IQ, system quality and service quality on the use of DL in the local context.

The Spirit of Adventure: A Driver of Attractiveness of the Hospitality Industry for Young People during a Pandemic Crisis

The COVID-19 pandemic has strongly affected tourism and leisure activities worldwide, especially in the hospitality and tourism sectors. Within this context, this study aimed to evaluate the impact of the pandemic on the future attractiveness of the hospitality industry (HI) to young people. The conceptual model underpinning the empirical research proposes a direct relationship between job attractiveness and the spirit of youth adventure. Findings prove that young people are enthusiastic about working in the HI because they can easily practice their creative ideas. Communicating with other people and dealing with clients’ complaints is challenging for them in the pandemic crisis created by COVID-19. The results are of interest to policymakers in terms of suggestions on how to transform the challenges into opportunities by using the constraints imposed by the pandemic crisis that have limited the socialisation between people, enhancing the creativity of young people, and motivating them to work in the HI. Moreover, our research provides managers and other decisionmakers with some motivational factors to increase the attractiveness of their companies to young people and suggestions helpful to scholars involved in HI research to cultivate resilience capabilities by giving them inherent skills.

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

Data science can create value by extracting structured and unstructured data using an appropriate algorithm. Data science operations have undergone drastic changes because of accelerated deep learning progress. Deep learning is an advanced process of machine learning algorithm. Its simple process of presenting data to the system is sharply different from other machine learning processes. Deep learning uses advanced analytics to solve complex problems for accurate business decisions. Deep leaning is considered a promising area for creating additional value in firms' productivity and sustainability as they develop their smart manufacturing activities. Deep learning capability can help a manufacturing firm's predictive maintenance, quality control, and anomaly detection. The impact of deep learning technology capability on manufacturing firms is an underexplored area in the literature. With this background, the purpose of this study is to examine the impact of deep learning technology capability on manufacturing firms with moderating roles of deep learning related technology turbulence and top management support of the manufacturing firms. With the help of literature review and theories, a conceptual model has been prepared, which is then validated with the PLS-SEM technique analyzing 473 responses from employees of manufacturing firms. The study shows the significance of deep learning technology capability on smart manufacturing systems. Also, the study highlights the moderating impacts of top management team (TMT) support as well as the moderating impacts of deep learning related technology turbulence on smart manufacturing systems.

What do urban and rural hotel managers say about the future of hotels after COVID-19? The new meaning of safety experiences

The pandemic crisis has caused a change in tourism trends that affect the way hotels are managed. In accordance with the United Nations (2020), hotels must guarantee safe experiences for customers by incorporating sustainability measures. Collaboration between health and tourism authorities and the tourism industry is key. To test this proposal among hotels in Spain, 3 online focus groups and 25 personal interviews with 36 urban and 28 rural hotels were held in order to define the indicators. The questionnaire was applied to a sample of 475 urban hotels out of 443 rural hotels. The conclusions were: 1.) While in urban areas the testing protocols, especially for workers, are followed by most hotels, in rural areas hotel managers do not consider it as a priority in daily activity due to the reduced contact they have. 2.) A change in trends in the sustainable management of both rural and urban hotels is justified. 3.) Urban and rural hotels are more likely to incorporate collaborative strategies with tourism and health authorities to reduce the negative impact of COVID-19. According to the estimates of the hotels, the implementation of these measures would help to start the recovery process of the hotel industry.

Knowledge Sharing through Social Networking Sites (SNSs): A Study of Pakistani Research Students

The present study investigated the knowledge-sharing behavior of library and information management researchers, using the lens of the theory of planned behavior. The study is quantitative and adopted a survey questionnaire as a data collection technique. The snowball sampling technique was considered suitable to recruit respondents to the study. Data were analyzed with the help of SPSS (20.0) and the ADANCO (2.0.1). The research findings confirm that subjective norms and perceived behavioral control have a significant impact on intentions to share knowledge, whereas knowledge sharing intentions have a statistically significant positive impact on knowledge sharing behavior through SNS among postgraduate students. Attitude towards knowledge sharing directly triggers knowledge sharing practices through social media networking sites. Intentions to share knowledge do not mediate the relationship of attitude and knowledge sharing behavior. The theory of planned behavior has widely been used to measure knowledge-sharing behavior in different sectors. However, the relationship between attitude, subjective norms, perceived behavior control, intentions to share knowledge within the domain of social media is explored first time in this study, particularly in the context of the library and information science post-graduate students.

Behavioral intention to use cryptocurrency in Malaysia: an empirical study

Purpose

The purpose of this study is to empirically examine the factors influencing consumer behavioral intention (BI) to use cryptocurrency as a medium of transaction. Constructs from the unified theory of acceptance and use of technology model and an added variable, perceived risk (PR), are examined to predict BI. Age and gender as moderators are retained in this model.

Design/methodology/approach

An online survey was used to gather the respondents’ responses on a five-point Likert scale. G * Power was used to calculate the required minimum sample size. A non-probability sampling technique was used to gather data from the 290 respondents based in Malaysia. The final data set was analyzed using the statistical package for the social sciences and SmartPLS software using structural equation modeling.

Findings

The results show that three of the five proposed factors (performance expectancy, effort expectancy and facilitating condition) are significant predictors of BI to adopt cryptocurrency as a medium of transaction. Interestingly, PR is not a significant predictor even though prior research studies showed otherwise. Likewise, the relationship between BI and social influence became significant only when age is added as a moderator.

Practical implications

Malaysians are still wary of cryptocurrency, even though global tech firms such as Amazon and Microsoft are already accepting Bitcoin as a payment method. This study aims to provide relevant authorities and businesses (i.e. central bank, retail merchants and cryptocurrency exchangers) insights toward understanding the factors consumers focus on if they were to use cryptocurrency as a medium of transaction.

Originality/value

Most cryptocurrency research are done in developed countries (i.e. USA, UK and EU) perspective. This research addresses the lack of quantitative literature on significant factors influencing BI to use cryptocurrency in developing country context while taking a PR, age and gender into consideration.

Lipidomics study of plasma from patients suggest that ALS and PLS are part of a continuum of motor neuron disorders

Motor neuron disorders (MND) include a group of pathologies that affect upper and/or lower motor neurons. Among them, amyotrophic lateral sclerosis (ALS) is characterized by progressive muscle weakness, with fatal outcomes only in a few years after diagnosis. On the other hand, primary lateral sclerosis (PLS), a more benign form of MND that only affects upper motor neurons, results in life-long progressive motor dysfunction. Although the outcomes are quite different, ALS and PLS present with similar symptoms at disease onset, to the degree that both disorders could be considered part of a continuum. These similarities and the lack of reliable biomarkers often result in delays in accurate diagnosis and/or treatment. In the nervous system, lipids exert a wide variety of functions, including roles in cell structure, synaptic transmission, and multiple metabolic processes. Thus, the study of the absolute and relative concentrations of a subset of lipids in human pathology can shed light into these cellular processes and unravel alterations in one or more pathways. In here, we report the lipid composition of longitudinal plasma samples from ALS and PLS patients initially, and after 2 years following enrollment in a clinical study. Our analysis revealed common aspects of these pathologies suggesting that, from the lipidomics point of view, PLS and ALS behave as part of a continuum of motor neuron disorders.

The effect of green HRM on employee green behaviors in higher education: the mediating mechanism of green work engagement

Purpose

Building on the social exchange theory (SET), this study aims to propose a model of the effects of green human resource management on employee in-role, extra-role and green innovative work behavior (GIWB). This study proposes, building on both the job demands-resources model and SET, that the aforementioned links can be explained through the mediating role of green work engagement (GWE).

Design/methodology/approach

Data were collected from employees (n. 208) working in Palestinian higher education organizations using a self-administered questionnaire. The partial least squares-structural equation modeling was the primary statistical technique adopted to examine the study’s hypotheses.

Findings

The results suggest that green human resources management (GHRM) was a significant predictor of employee in-role green behavior, extra-role green behavior and GIWB. Furthermore, GWE demonstrated to be a significant intervening mechanism to explain the above-mentioned relationships.

Practical implications

The results provide useful insights for higher education policymakers on how GHRM may positively contribute to employee green outcomes.

Originality/value

This paper is novel for several reasons. First, it contributes to the general literature of GHRM. Second, it contributes to the limited body of knowledge on GHRM in the context of higher education. Third, the distinct contribution of this study is the introduction of GIWB as an outcome of GHRM, and GWE as a mediating variable in the relationship between GHRM and employee green behaviors.

Navigating from Brand Image to Patient Loyalty: Mediating Effect of Service Quality and Patient Satisfaction

Purpose: This study aims to analyse brand image (BI)–service quality (SQ)–patient satisfaction (PS)–loyalty in healthcare by understanding BI and SQ modelled as multidimensional constructs in Indian healthcare. Furthermore, the mediating effect from BI to loyalty through SQ and PS is examined.

Design/methodology/approach: Analysis of survey data of 210 patients from Indian hospitals reveals that BI directly affects SQ, PS and loyalty, that SQ directly influences PS, that PS directly influences loyalty of patients and that PS and BI are significant determinants of loyalty.

Findings: Our findings suggest that BI has both a direct and indirect (through SQ and PS) impact on loyalty of patients. Moreover, the mediation of BI to loyalty through SQ and PS is partial and complementary mediation.

Practical implications: By understanding the relationship between BI, SQ, PS and loyalty, managers will be able to better understand the mechanism to enhance loyalty. As BI impacts loyalty indirectly through SQ and PS, managers need to improve quality, which will enhance satisfaction of patients and thus help in the formation of loyal patients.

Originality/value: This study shows how the mediating effect of SQ and PS can be used to improve patient loyalty by building positive BI.

Clinical implications of serum neurofilament in newly diagnosed MS patients: A longitudinal multicentre cohort study

Background

We aim to evaluate serum neurofilament light chain (sNfL), indicating neuroaxonal damage, as a biomarker at diagnosis in a large cohort of early multiple sclerosis (MS) patients.

Methods

In a multicentre prospective longitudinal observational cohort, patients with newly diagnosed relapsing-remitting MS (RRMS) or clinically isolated syndrome (CIS) were recruited between August 2010 and November 2015 in 22 centers. Clinical parameters, MRI, and sNfL levels (measured by single molecule array) were assessed at baseline and up to four-year follow-up.

Findings

Of 814 patients, 54.7% (445) were diagnosed with RRMS and 45.3% (369) with CIS when applying 2010 McDonald criteria (RRMS[2010] and CIS[2010]). After reclassification of CIS[2010] patients with existing CSF analysis, according to 2017 criteria, sNfL levels were lower in CIS[2017] than RRMS[2017] patients (9.1 pg/ml, IQR 6.2–13.7 pg/ml, n = 45; 10.8 pg/ml, IQR 7.4–20.1 pg/ml, n = 213; p = 0.036). sNfL levels correlated with number of T2 and Gd+ lesions at baseline and future clinical relapses. Patients receiving disease-modifying therapy (DMT) during the first four years had higher baseline sNfL levels than DMT-naïve patients (11.8 pg/ml, IQR 7.5-20.7 pg/ml, n = 726; 9.7 pg/ml, IQR 6.4–15.3 pg/ml, n = 88). Therapy escalation decisions within this period were reflected by longitudinal changes in sNfL levels.

Interpretation

Assessment of sNfL increases diagnostic accuracy, is associated with disease course prognosis and may, particularly when measured longitudinally, facilitate therapeutic decisions.

Funding

Supported the German Federal Ministry for Education and Research, the German Research Council, and Hertie-Stiftung.

Organizational Learning and Corporate Social Responsibility Drivers of Performance in SMEs in Northwestern Mexico

The main objective of this article is to examine the effects of the organizational learning and corporate social responsibility (social, economic and environmental) practices that exert on the financial performance of SMEs (small and medium enterprises). The research is based on a sample of 343 companies of which 19.8% belong to the primary sector, 26.6% to the secondary sector and 53.6% to the tertiary sector of the northwestern region of Mexico. The data were collected from February to May 2018 with the support of a self-directed structured survey of company managers. For the analysis and validation of the results, the statistical technique of the structural equations model (SEM) based on the variance was used to validate the structured relationships in this investigation through PLS (partial least squares). These analyses were prepared with the support of SmartPLS version 3.2.8 Professional. The results indicate that OLE (organizational learning) is a key element to strengthen CSR (corporate social responsibility) practices and increase financial performance in these types of companies, and that CSR is key to increasing financial performance. In addition, it was found that the OLE is a variable that mediates the relationship between CSR and financial performance. The work contributes to the development of the literature of organizational learning, the theory of resources and capabilities, and stakeholder theory

Intrathecal B-cell accumulation and axonal damage distinguish MRI-based benign from aggressive onset in MS

Objective

We explored the incremental value of adding multiple disease activity biomarkers in CSF and serum for distinguishing MRI-based benign from aggressive MS in early disease course.

Methods

Ninety-three patients diagnosed with clinically isolated syndrome (CIS) or early MS were divided into 3 nonoverlapping severity groups defined by objective MRI criteria. Ninety-seven patients with noninflammatory neurologic disorders and 48 patients with other inflammatory neurologic diseases served as controls. Leukocyte subsets in the CSF were analyzed by flow cytometry. CSF neurofilament light chain (NfL) and chitinase-3-like protein 1 (CHI3L1) levels were measured by ELISA. Serum NfL levels were examined using single molecule array technology.

Results

CSF CD20+/CD14+ ratios and NfL levels in CSF and serum were significantly different between high and low MRI severity groups, whereas no difference was found for CSF CHI3L1 levels. NfL levels in CSF and serum highly correlated. Receiver operating characteristic analysis demonstrated that the cumulative sums combining CSF CD20+/CD14+ ratios and NfL levels in serum or CSF considerably improved diagnostic accuracy. A composite score built from these 2 cumulative sums best distinguished MRI severity. These findings were validated by support vector machine analysis, which confirmed that the accuracy of the cumulative sums and composite score outperforms single biomarkers.

Conclusion

Patients with extreme manifestations of CIS or early MS defined by strict MRI parameters can be best distinguished by combining markers of intrathecal B-cell accumulation and axonal damage. This could stratify individual treatment decisions toward a more personalized immunotherapy.

Heritage Image and Attitudes toward a Heritage Site: Do They Really Mediate the Relationship between User-Generated Content and Travel Intentions toward a Heritage Site?

Traveling to heritage sites represents a growing sector with great potential in the context of heritage tourism, which is a relevant topic but has received little scholarly attention. The present study tries to bridge this gap by investigating the mediating role of heritage image and attitudes toward a heritage site in the relationship between user-generated content (UGC) and travel intention toward a heritage site. In particular, we applied partial least squares structural equation modeling (PLS-SEM), which is a relatively popular new method, instead of the traditionally used covariance-based structural equation modeling (CB-SEM) method. The rise in popularity of PLS-SEM has been particularly noticeable since 2013, but in the area of heritage tourism, research is still in an early stage of development because the heritage discipline has hardly exploited the benefits of this approach. We proposed a multimediation model for heritage tourism to address this relatively new research avenue. It was discovered that exogenous variables directly and indirectly influenced travel intentions toward a heritage site through their mediators (i.e., heritage image and attitudes toward a heritage site). The findings provide an appropriate empirical and theoretical conclusion as well as valuable insights for heritage management organizations (HMOs) into the performance of heritage tourism, both for local professionals and the host communities that assist in increasing the development of heritage tourism.

Consistent Partial Least Squares Path Modeling via Regularization

Partial least squares (PLS) path modeling is a component-based structural equation modeling that has been adopted in social and psychological research due to its data-analytic capability and flexibility. A recent methodological advance is consistent PLS (PLSc), designed to produce consistent estimates of path coefficients in structural models involving common factors. In practice, however, PLSc may frequently encounter multicollinearity in part because it takes a strategy of estimating path coefficients based on consistent correlations among independent latent variables. PLSc has yet no remedy for this multicollinearity problem, which can cause loss of statistical power and accuracy in parameter estimation. Thus, a ridge type of regularization is incorporated into PLSc, creating a new technique called regularized PLSc. A comprehensive simulation study is conducted to evaluate the performance of regularized PLSc as compared to its non-regularized counterpart in terms of power and accuracy. The results show that our regularized PLSc is recommended for use when serious multicollinearity is present.