Perceived Intrusiveness vs. Relevance: A PLS-SEM Analysis of Personalized Advertising in Morocco

Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example

Measurement errors wield the potential to introduce uncertainties and inaccuracies, casting shadows on data quality and jeopardizing the integrity of structural relationships. Notably robust against measurement errors, Partial Least Squares Structural Equation Modelling (PLS-SEM) has historically maintained a reputation for resilience. However, recent insights have unveiled its susceptibility to these errors, instigating a revaluation of its standing in the Structural Equation Modelling landscape. Overlooking measurement errors in PLS-SEM carry consequential repercussions, notably tainting the accuracy of structural relationships and introducing bias. This effect becomes particularly pronounced when dealing with an insufficient understanding of the intricate structural dynamics. Unfortunately, PLS-SEM currently lacks an all-encompassing remedy to address this concern. Consequently, the quantification of measurement errors impact in PLS-SEM gains paramount importance, fostering a demand for innovative strategies to propel its effectiveness forward. Notably, contemporary investigations have unmasked PLS-SEM's vulnerability to non-orthogonal errors. This revelation challenges the notion of its imperviousness to the detrimental influence of measurement errors, necessitating a comprehensive evaluation of its performance under such conditions. This study leveraged simulated data to extract empirical findings and employed parameters biasedness analysis. This analysis led to the determination that the stability of the PLS-SEM algorithm is compromised when exposed to diverse measurement error scenarios. Consequently, the outcomes generated exhibit both instability and bias. This bias becomes increasingly conspicuous as the magnitude of measurement errors intensifies. Thus, the study proposes avenues for elevating the robustness of PLS-SEM.

Background: Patient satisfaction is used as an important marker of the quality of medical services. This has an impact on patient loyalty and increases patient retention. Generally, perceptions of the quality of hospital services are based on patients' assessments of the services provided by the hospital, for example the relationship between patients and nurses, doctors and staff. Purpose: To determine factors related to general patient satisfaction. Method: Quantitative research with hypothesis testing without special treatment of subjects during the research period. Data was obtained through a survey of all patients who visited the emergency room at Budi Mulia Bitung Hospital in January 2024, totaling 170 respondents. The sample in this study was taken using a non-probability sampling method with the criteria, being more than 19 years old, cooperative, having a cell phone, and being able to be guided to fill out an online questionnaire. Results: All indicators in each dimension have an outer loading value of >0.7 and an AVE value for satisfaction with doctor services (0.703), emergency staff (0.860), emergency environment (0.656), general patient satisfaction (0.674). Cronbach's alpha and Composite Reliability values for all variables are above 0.7. The R-Square value for the general satisfaction variable is 0.948 and behavioral intention (0.944), so it can be said to be overfit. Based on the t-statistic value, p-value, and path coefficient, all variables in the research model tested are all significant. Conclusion: Satisfaction with physician services, emergency department staff, and emergency department environment are factors that are associated with general patient satisfaction and behavioral intentions. Keywords: Behavioral Intention; Patient Satisfaction; Partial Least Squares Structural Equation Modeling (PLS-SEM) Pendahuluan: Kepuasan pasien digunakan sebagai penanda penting untuk kualitas layanan medis. Hal ini berdampak pada loyalitas pasien dan meningkatkan retensi pasien. Umumnya persepsi kualitas pelayanan rumah sakit didasarkan pada penilaian pasien terhadap pelayanan yang diberikan rumah sakit, misalnya hubungan antara pasien dan perawat, dokter dan staf. Tujuan: Untuk mengetahui faktor-faktor yang berhubungan dengan general satisfaction patient. Metode: Penelitian kuantitatif dengan pengujian hipotesis tanpa perlakuan khusus terhadap subjek selama periode penelitian. Data yang diperoleh melalui survei kepada seluruh pasien yang berkunjung ke IGD RS. Budi Mulia Bitung pada bulan Januari 2024 sebanyak 170 responden. Sampel dalam penelitian ini diambil menggunakan metode non-probability sampling dengan kriteria, sudah berusia lebih dari 19 tahun, kooperatif, memiliki handphone, dan dapat dipandu untuk mengisi kuesioner online. Hasil: Semua indikator pada setiap dimensi memiliki nilai outer loading >0.7 dan nilai AVE physician care satisfaction (0.703), emergency department staff (0.860), emergency department environment (0.656), general satisfaction patient (0.674). Nilai cronbach’s alpha dan composite reliability pada semua variabel telah berada di atas 0.7. Nilai R-Square pada variabel kepuasan umum sebesar 0.948 dan niat berperilaku (0.944), sehingga dapat dikatakan overfit. Berdasarkan nilai t-statistik, nilai-p, dan koefisien jalur seluruh variabel dalam model penelitian yang diuji semuanya signifikan. Simpulan: Physician care satisfaction, emergency department staff, dan emergency department environment merupakan faktor-faktor yang berhubungan dengan general satisfaction patient dan behaviour intention. Kata Kunci: Behavioral Intention; Partial Least Squares Structural Equation Modeling (PLS-SEM); Patient Satisfaction.

In today's competitive business environment, operational efficiency is crucial for organizations to maintain a competitive edge. The integration of automation and the Internet of Things (IoT) has emerged as a transformative approach to streamline processes and enhance productivity. However, the synergistic impact of these technologies on operational efficiency remains underexplored. This study aims to evaluate the individual and combined effects of automation and IoT on operational efficiency. It seeks to provide empirical evidence on how these technologies contribute to optimizing workflows and decision-making processes. Methodology Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from organizations across multiple industries. Constructs were measured through validated survey instruments, and hypotheses were tested for direct and synergistic effects. The findings indicate that automation significantly enhances operational efficiency by reducing errors and improving process consistency. IoT adoption complements this by enabling real-time insights and improved decision-making. The combined implementation of these technologies demonstrates a moderate synergistic effect, amplifying operational gains. This study underscores the transformative potential of integrating automation and IoT. By leveraging their complementary strengths, organizations can achieve higher levels of efficiency, providing valuable guidance for digital transformation strategies. In today's competitive business environment, operational efficiency is crucial for organizations to maintain a competitive edge. The integration of automation and the Internet of Things (IoT) has emerged as a transformative approach to streamline processes and enhance productivity. However, the synergistic impact of these technologies on operational efficiency remains underexplored. This study aims to evaluate the individual and combined effects of automation and IoT on operational efficiency. It seeks to provide empirical evidence on how these technologies contribute to optimizing workflows and decision-making processes. Methodology Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from organizations across multiple industries. Constructs were measured through validated survey instruments, and hypotheses were tested for direct and synergistic effects. The findings indicate that automation significantly enhances operational efficiency by reducing errors and improving process consistency. IoT adoption complements this by enabling real-time insights and improved decision-making. The combined implementation of these technologies demonstrates a moderate synergistic effect, amplifying operational gains. This study underscores the transformative potential of integrating automation and IoT. By leveraging their complementary strengths, organizations can achieve higher levels of efficiency, providing valuable guidance for digital transformation strategies.

In management accounting research, the capabilities of Partial Least Squares Structural Equation Modelling (PLS-SEM) have only partially been utilized. These yet unexploited capabilities of PLS-SEM are a useful tool in the often explorative state of research in management accounting. After reviewing eleven top-ranked management accounting journals through the end of 2013, 37 articles in which PLS-SEM is used are identified. These articles are analysed based on multiple relevant criteria to determine the progress in this research area, including the reasons for using PLS-SEM, the characteristics of the data and the models, and model evaluation and reporting. A special focus is placed on the degree of importance of these analysed criteria for the future development of management accounting research. To ensure continued theoretical development in management accounting, this article also offers recommendations to avoid common pitfalls and provides guidance for the advanced use of PLS-SEM in management accounting research.

Researchers across a wide range of disciplines exploited the capabilities of partial least squares structural equation modeling (PLS-SEM). The rise in popularity of PLS-SEM is particularly noticeable 2013 onwards. The banking and finance discipline, however, hardly exploits the advantages of the PLS-SEM approach. PLS-SEM can be used for prediction and exploration in complex models with relaxed expectations on data. PLS-SEM is useful in identifying relationships between constructs. If the primary objective is theory development, PLS-SEM is appropriate.

Partial Least Squares Structural Equation Modeling (PLS-SEM) serves as a comprehensive methodological framework, critically addressing theoretical underpinnings, rigorous analytical approaches, and state-of-the-art modeling techniques vital for contemporary business research. The methodological discussion includes detailed exploration of reflective and formative measurement models, structural model specification, reliability, and validity assessments, alongside advanced analytical methods such as Confirmatory Tetrad Analysis (CTA-PLS) and Importance-Performance Matrix Analysis (IPMA). Advanced algorithms including bootstrapping and blindfolding procedures are elaborated, emphasizing predictive relevance and methodological precision. Partial Least Squares Structural Equation Modeling further offers robust analytical capabilities to evaluate modern AI-driven innovations, facilitating sophisticated assessment of user trust, perceived accuracy, and satisfaction with recommender systems, voice assistants, autonomous vehicles, AI-driven healthcare diagnostics, personalized educational platforms, and fraud detection technologies. Ethical considerations, reporting best practices, computational tools (SmartPLS, SEMinR), and Explainable AI (XAI) integration enhance the comprehensive nature of this framework. Furthermore, integration of cutting-edge analytical approaches such as moderation, mediation, Multi-Group Analysis (MGA), nonlinear modeling, machine learning integration, and quantum computing potential positions PLS-SEM as indispensable for contemporary business and technology research, ultimately promoting actionable scholarly insights and ensuring maximum methodological impact.

Construction and demolition waste (CDW), as the main consequence of construction and demolition (C&D) activities, has severely affected our sustainability needs. However, construction and demolition waste management (CDWM) lacks the integration of sustainability concepts. Thus, there is a great need to include sustainability dimensions in CDWM to reach sustainable construction and demolition waste management (SCDWM). This study aims at empirically investigating SCDWM by analyzing the impacts of factors that contribute to sustainability aspects of CDWM on waste management hierarchy (WMH), including reduce, reuse, recycle, and disposal strategies. According to the literature, 26 factors were initially identified and grouped under four categories, namely environmental, economic, social, and administrative, that contribute to sustainability aspects of CDWM. Subsequently, a pilot test was performed to assess the significance and applicability of these factors in the Malaysian construction industry by implementing the express analytic hierarchy process (EAHP). Then, a questionnaire survey was performed to collect data from 132 construction companies involved in CDWM. Partial least squares-structural equation modeling (PLS-SEM) was used to test the hypothetical relationships by applying SmartPLS software. Results demonstrated that the economic aspect of CDWM (main category) and “public environment contamination due to illegal waste dumping” (sub-category) were the most influential factor in SCDWM in Malaysia.

This research aims to investigate the factors that influence the effectiveness of Information Systems (IS) Governance in Higher Education Institutions (IPT) using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The background of this research reflects the importance of IS in supporting operations, management and decision making in a higher education environment that is increasingly complex and dependent on technology.The PLS-SEM method analyzes the relationship between key variables that influence the effectiveness of IS governance at IPT. It is a powerful multivariate statistical approach that allows factor analysis and regression in a single framework, allowing researchers to holistically understand how factors relate to each other. The results of this research will likely provide valuable insight for decision-makers at IPT in improving IS management and utilization. Practical implications include the development of more effective policies, better management strategies, and improved IS infrastructure. In addition, this research is also expected to provide an essential contribution to academic literature in understanding the factors that influence the effectiveness of IS governance in the higher education context. By better understanding the factors that influence the effectiveness of IS governance, IPT can increase its competitiveness, improve the quality of educational services, and support the achievement of its strategic goals. This research is expected to significantly contribute to understanding how IS governance can be implemented and managed more effectively in higher education environments through the PLS-SEM approach.

Voluminous studies use Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data. One of the reasons for using PLS-SEM is when the structural model is complex. Studies employing complex structural models with many constructs and indicators lead to PLS-SEM selection for the analysis. The purposes of assessing the measurement model are to examine basic dimensions for construct variables, validate the dimensions, and determine the number of dimensions for each construct. Assessment of measurement model includes composite reliability and average variance extracted (AVE) to assess reliability and validity, respectively. This study tests the validity and reliability of the attitude construct in the context of compliance behavior of income zakat that other studies can use. This study assesses the measurement model to examine basic dimensions for construct variables, validate the dimensions, and determine the number of dimensions for each construct. Assessment of measurement model includes composite reliability and average variance extracted (AVE) to assess reliability and validity, respectively. This study hopes future research can adapt and adopts the attitude items used in this study in their future research.

Purpose The construction industry (CI) plays a pivotal role in economic growth but is also a significant contributor to environmental degradation due to high resource consumption, energy use and waste generation. This highlights the urgent need to transition from the traditional linear model to a Circular Economy (CE) framework, which emphasizes resource efficiency, waste minimization and sustainability. However, the adoption of CE in the construction sector remains limited due to a lack of structured understanding of the external determinants influencing its implementation. This study addresses this gap by applying the PESTEL (political, economic, social, technological, environmental and legal) framework to identify and evaluate the macro-environmental determinants that impact CE adoption in the CI. Using a hybrid methodology that combines the Analytic Hierarchy Process (AHP) and Partial Least Squares Structural Equation Modeling (PLS-SEM), the study quantifies the influence of each factor. The results indicate that environmental, political and economic aspects significantly drive CE adoption, while social and technological factors are essential for operationalizing CE practices at scale. Legal factors had a relatively lesser impact. The study presents a comprehensive and data-driven approach to understanding CE implementation, offering actionable insights for policymakers, industry practitioners and researchers. It emphasizes the need for coordinated efforts across multiple dimensions, enabling targeted interventions that support the shift toward sustainable construction practices. Design/methodology/approach The study employs a mixed-methods approach, utilizing the Analytic Hierarchical Process (AHP) and Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the external factors influencing CE adoption in the built environment. Data were collected through a structured questionnaire survey targeting key stakeholders, including project managers, engineers, contractors, architects and material suppliers. Findings The results indicate that environmental factors, government policies and economic feasibility are the most significant determinants for CE adoption in the CI. Additionally, social awareness, technological advancements and a supportive legal framework emerge as crucial enablers for promoting sustainable practices. Research limitations/implications This study is limited by its reliance on questionnaire data, which may be influenced by respondent bias and varying levels of awareness about Circular Economy (CE) practices. The sample is geographically constrained, which may affect the generalizability of the findings. Additionally, the focus is on external PESTEL factors, without considering internal organizational influences. Future research could address these gaps by expanding the study scope and exploring both internal and external drivers of CE adoption. Practical implications The findings offer actionable insights for industry stakeholders to enhance CE adoption by prioritizing resource efficiency, strengthening regulatory frameworks, increasing technological investments and fostering collaboration within the CI. Originality/value This study provides a comprehensive evaluation of external determinants shaping CE adoption in the CI, contributing to the literature on sustainable construction. The results serve as a roadmap for policymakers and industry professionals to guide the transition toward a more circular and environmentally friendly construction model.

Aims: The current global business landscape demands an acute commitment of international businesses’ sustainable practices to attain a global sustainability agenda. This paper examines the intricate nexus of Multinational Enterprises' (MNEs) Corporate Social Responsibility (CSR) practices, particularly environmental innovation, business ethics, CSR reporting and their environmental performance, social performance, as well as the influence of their alignment with the United Nations Sustainable Development Goals (SDGs). Study Design: This quantitative study employed a Partial Least Squares Structural Equation Modeling (PLS-SEM). Place and Duration of Study: The study used data from Refinitiv Eikon workspace for 91 MNEs from the Middle East and North Africa (MENA) in year 2023. Methodology: The study used secondary data from Refinitiv Eikon workspace for 91 MNEs from MENA region using the Partial Least Squares Structural Equation Modeling (PLS-SEM). Results and Conclusion: The PLS-SEM’s results underline the potential and significant involvement of international businesses in shaping sustainable development, especially in developing and emerging countries. The findings further show that MNEs’ CSR practices particularly environmental and social performance and their adoption or alignment with the United Nations’ SDGs emerges as a pivotal approach to enhance MNEs’ contribution to MENA countries’ sustainable development and global sustainability agenda.

Although the number of empirical applications of partial least–squares structural equation modeling (PLS-SEM) in tourism has increased in the last two years, Assaker, Huang, and Hallak have conducted the only assessment on the use of PLS-SEM in four studies and with a limited number of criteria. Thus, this study aims to critically analyze how the PLS-SEM method has been applied in 44 articles published in 11 leading tourism journals from 2000 to 2014 in terms of four key criteria: (1) themes explored and main motivations for using PLS-SEM; (2) characteristics of proposed models; (3) how the models were evaluated; and (4) the use of more advanced analyses within the method. The findings revealed that although applications in tourism have improved in recent years, problematic aspects in the application of PLS-SEM in tourism research still exist. The article provides suggestions on how to improve the use of PLS-SEM in future tourism applications.

Comprehensively clarifying the influencing factors of carbon emissions is crucial to realizing carbon emission reduction targets in China. To address this issue, this paper develops a four-level carbon emission influencing factor system from six perspectives: population, economy, energy, water resources, main pollutants, and afforestation. To analyze how these factors affect carbon emissions, we propose an improved partial least squares structural equation model (PLS-SEM) based on a random forest (RF), named RF-PLS-SEM. In addition, the entropy weight method (EWM) is employed to evaluate the low-carbon development level according to the results of the RF-PLS-SEM. This paper takes Shandong Province as an example for empirical analysis. The results demonstrate that the improved model significantly improves accuracy from 0.8141 to 0.9220. Moreover, water resources and afforestation have relatively small impacts on carbon emissions. Primary and tertiary industries are negative influencing factors that inhibit the growth of carbon emissions, whereas total energy consumption, the volume of wastewater discharged and of common industrial solid waste are positive and direct influencing factors, and population density is indirect. In particular, this paper explores the important role of fisheries in reducing carbon emissions and discusses the relationship between population aging and carbon emissions. In terms of the level of low-carbon development, the assessment system of carbon emission is constructed from four dimensions, namely, population, economy, energy, and main pollutants, showing weak, basic, and sustainable stages of low-carbon development during the 1997–2012, 2013–2020, and 2021–2022 periods, respectively.

ABSTRACTWith the increasing emphasis on global social responsibility, cause‐related marketing (CRM) has emerged as a pivotal strategy for enterprises to harmonize business objectives with social benefits. However, in practice, many cause projects fail to effectively elicit consumer responses. Moreover, most existing studies focus on singular attributions, providing limited systematic insights into the synergistic effects of cause attributes, individual traits, and corporate cues within cause projects. This research combines Partial Least Squares Structural Equation Modeling (PLS‐SEM) with Fuzzy Set Qualitative Comparative Analysis (fsQCA) to explore the effects of cause's attributes, self‐construal, and consumer‐company identity (CCI) on consumer purchase intention through an analysis of 426 qualifying questionnaires. The PLS‐SEM analysis results confirm that the three cause's attributes exert significant positive effects on purchase intention. Interdependent self‐construal (ITSC) indirectly influences purchase intention through CCI, whereas independent self‐construal (ISC) directly promotes purchase behavior. Furthermore, CCI serves as a critical intermediary in the relationship between cause proximity (CP) and brand‐cause fit (BCF). The fsQCA analysis further revealed four distinct configuration paths associated with high purchase intention, underscoring the significance of multi‐factor synergies. This study not only offers a multidimensional explanatory framework for CRM theory but also provides actionable path options for enterprise practice.