Two-stage Modeling Approach Research Articles

Exploring users' fluidity and adaptation in virtual worlds: metaverse features and interaction of awe and realism

PurposeMetaverse technology has attracted much attention in many contexts, including industry, education, marketing and business. Some recent studies have focused on qualitative studies based on the actual definition of the metaverse. However, practical research related to metaverse platforms remains in its infancy. This study provides actionable insights into the determinants of metaverse adoption by using perceived fluidity.Design/methodology/approachA two-stage structural equation modeling (SEM) approach and Hayes’ Macro approach are used to examine the proposed hypotheses.FindingsResults show that technology features (e.g. real-time rendering, interactivity and immersion) increase users’ perceived fluidity, which in turn leads to positive intentions to use the metaverse. A high level of perceived realism is not an advantage for metaverse technology and plays a negative moderating role in this mechanism. The interaction of awe with technological features can enhance the negative moderating effects of realism.Originality/valueThis study pioneers the examination of perceived fluidity as a key determinant of metaverse adoption, offering a novel perspective beyond traditional factors. It uniquely identifies the paradoxical role of perceived realism, demonstrating its potential negative impact on user experience. In addition, the research highlights the reinforcing effect of awe on this relationship.

A two-stage spatial prediction modeling approach based on graph neural networks and neural processes

Spatial prediction models hold significant application value in fields such as environmental science, economic development, and geological exploration. With advancements in deep learning technology, graph neural networks (GNNs) offer a powerful and scalable solution for spatial data modeling. In these models, each spatial location is represented as a vertex, with the explanatory variables at each point converted into vertex features, and the vertex label corresponding to the target value at that point. GNNs utilize this representation to predict vertex labels, thereby performing spatial prediction. However, the residuals of GNN predictions are found to still exhibit spatial autocorrelation, indicating that traditional GNNs only achieve suboptimal results in terms of capturing complex spatial relationships. In this paper, we propose a two-stage spatial prediction method called Location Embedded Graph Neural Networks-Residual Neural Processes (LEGNN-RNP) to address this challenge. In the first stage, we employ LEGNNs, a new framework that integrates location features into GNNs through an attention mechanism, enhancing the learning of complex spatial models by considering both spatial context and attribute features. In the second stage, we model the residuals from LEGNN predictions to further extract spatial patterns from the data. Specifically, we introduce RNP, a neural process-based approach to model the Gaussian Process (GP) distribution of residuals. The distribution parameters (i.e., mean and covariance) are parameterized by a neural network, where the mean estimates the residual and the variance quantifies the prediction uncertainty. Experiments on four datasets demonstrate that our proposed two-stage method achieves state-of-the-art results by effectively extracting spatial relationships and significantly improving spatial prediction accuracy.

Study examining the significant role of intellectual property protection in driving radical technological innovation among national research project teams, employing PLS-SEM and ANN modeling.

This study examines the role of intellectual property protection (IPP) in enhancing radical technological innovation (RTI) within national research project teams, using an innovation-driven theory and an ability-motivation-opportunity (AMO) perspective. This study utilizes a sample of 336 national research project team members from various Chinese universities, research institutes, and corporations to analyze the theoretical model. Additionally, a two-stage hybrid partial least squares structural equation modeling (PLS-SEM) approach, combined with artificial neural network techniques (ANN), is employed to evaluate the hypotheses. The empirical findings of this study reveal a positive association between the intensity of IPP and RTI within national research project teams. Research and development investment intensity (R&DII) is identified as the primary predictor, while integrated leadership (IL) and group potential (GP) play crucial moderating roles. These groundbreaking findings extend the scope of innovation-driven and AMO theories, providing a proactive model for national research project teams to propose improvements to the IPP system, ultimately enhancing the realization of RTI.

Conscientiousness on Affective Commitment to Change: The Mediating Role of Psychological Ownership

Based on self-determination theory, this study identified the effect of conscientiousness on the affective commitment to change of lecturers in Indonesian universities. This study empirically examined the uniqueness of conscientiousness which is strongest and most consistently associated with various outcomes. The mediation effect of psychological ownership of lecturers was also examined. A total of 321 responses were analyzed using a two-stage structural equation modeling (SEM) approach to confirm factors and test the study hypotheses. The results showed that the level of conscientiousness had a significant effect on psychological ownership. Conscientiousness had a significant indirect effect on affective commitment to change only through the psychological ownership of the lecturers. These results support the mediating role of lecturers' psychological ownership level in explaining the influence between conscientiousness and the level of affective commitment to change for lecturers.

Pleasure and Satisfaction as Predictors of Future Cigarette and E-cigarette Use: A Novel Two-Stage Modeling Approach.

Subjective experience of e-cigarettes may be an important factor in helping people who use combustible cigarettes switch completely to e-cigarettes to reduce harm from smoking. This paper describes a novel two-stage analysis using pleasure and satisfaction responses from ecological momentary assessments (EMA) of both cigarette and e-cigarette use to predict future cigarette and e-cigarette tobacco use. This observational study included adult users of cigarettes and e-cigarettes who provided 7 days of EMA, capturing cigarette and e-cigarette use, followed by biweekly reports of cigarette and e-cigarette use over 1 year. Participants were 279 adults who provided both cigarette and e-cigarette responses during the EMA. We employed a two-stage analytic approach in which EMA data were used to predict subsequent levels of cigarette and e-cigarette use. In the first stage, EMA responses to cigarette and e-cigarette events were modeled via a mixed-effects location scale model to yield summaries of participants' means and variability on event-related ratings of pleasure and satisfaction. These EMA summaries served as predictors in the second stage analysis of the biweekly post-EMA longitudinal cigarette and e-cigarette use data. EMA pleasure and satisfaction ratings were similar for both products and predicted both longitudinal cigarette and e-cigarette use, even after controlling for baseline cigarette and e-cigarette dependence. Relatively higher levels of satisfaction with e-cigarettes were associated with greater decreases in cigarette use over time. Pleasure and satisfaction are important predictors of subsequent cigarette and e-cigarette use. Experienced subjective pleasure and satisfaction from e-cigarettes relative to cigarettes may be an important factor in helping individuals who smoke to switch completely to e-cigarettes as a harm reduction approach. In order to help sustain complete product switching and reduce dual use or relapse to smoking, e-cigarettes may need to deliver more satisfaction to the user compared to that experienced from cigarettes.

Testing the waters! The role of ethical leadership towards innovative work behavior through psychosocial well-being and perceived organizational support

PurposeIn response to the dynamic demands of the contemporary business landscape, this study critically examines the pivotal role of ethical leadership in shaping employee’s innovative behavior within organizations. Our research delves into the nuanced interplay between ethical leadership, psychological well-being and innovative work behavior. Drawing from the principles of social exchange theory, our study addresses a critical gap in the literature by exploring the mediating role of psychological well-being in the relationship between ethical leadership and employees' innovative work behavior.Design/methodology/approachIn this quantitative research, data were collected from 384 employees and their direct supervisors in Pakistan’s IT sector using “Google Forms” through a convenience sampling method facilitated by the “LinkedIn” platform. Additionally, the study applied a two-stage structural equation modeling approach, first to assess the uni-dimensionality, and subsequently, to evaluate the proposed hypotheses.FindingsThe research results unveiled a robust and positive impact of ethical leadership on innovative work behavior, operating through both direct and indirect pathways mediated by psychological well-being. Intriguingly, the moderating role of perceived organizational support adds depth to our understanding, revealing nuanced conditions under which ethical leadership influences employees' well-being and, subsequently, their innovative contributions.Practical implicationsBeyond theoretical contributions, our study provides practical insights for managers seeking to leverage employees' innovative work behavior for organizational success. By emphasizing ethical leadership as a catalyst, we advocate for its integration into HRM practices. However, recognizing the contextual nature of organizational support, our findings underscore the importance of adaptable leadership strategies to maximize positive outcomes.Originality/valueGrounded in the principles of social exchange theory, this research marks a pioneering effort to shed light on the link between ethical leadership and innovative work behavior through the mediation of psychological well-being. Additionally, this study makes a valuable contribution to the current body of knowledge by investigating the contingent influence of perceived organizational support on the relationship between ethical leadership and employees' psychological well-being.

Nonlinear modeling of oral glucose tolerance test response to evaluate associations with aging outcomes.

As people age, their ability to maintain homeostasis in response to stressors diminishes. Physical frailty, a syndrome characterized by loss of resilience to stressors, is thought to emerge due to dysregulation of and breakdowns in communication among key physiological systems. Dynamical systems modeling of these physiological systems aims to model the underlying processes that govern response to stressors. We hypothesize that dynamical systems model summaries are predictive of age-related declines in health and function. In this study, we analyze data obtained during 75-gram oral-glucose tolerance tests (OGTT) on 1,120 adults older than 50 years of age from the Baltimore Longitudinal Study on Aging. We adopt a two-stage modeling approach. First, we fit OGTT curves with the Ackerman model-a nonlinear, parametric model of the glucose-insulin system-and with functional principal components analysis. We then fit linear and Cox proportional hazards models to evaluate whether usual gait speed and survival are associated with the stage-one model summaries. We also develop recommendations for identifying inadequately-fitting nonlinear model fits in a cohort setting with numerous heterogeneous response curves. These recommendations include: (1) defining a constrained parameter space that ensures biologically plausible model fits, (2) evaluating the relative discrepancy between predicted and observed responses of biological interest, and (3) identifying model fits that have notably poor model fit summary measures, such as [Formula: see text], relative to other fits in the cohort. The Ackerman model was unable to adequately fit 36% of the OGTT curves. The stage-two regression analyses found no associations between Ackerman model summaries and usual gait speed, nor with survival. The second functional principal component score was associated with faster gait speed (p<0.01) and improved survival (p<0.01).

Chinese Overseas Foreign Direct Investment and Online Digital Freedom in Developing Countries

Although studies have shown China’s growing influence on developing countries’ policies, little empirical work has considered the relationship between Chinese overseas foreign direct investment (FDI) and host countries’ online digital freedom. Considering as many as 112 developing countries from 2003 to 2019, and using a two-stage least squares selection modeling approach, we find an association between Chinese FDI and four types of limits on online digital freedom. Conversely, when we substitute global FDI (excluding Chinese FDI), we obtain different results, suggesting there is something unique about Chinese multinational corporations and online digital freedom. Our research indicates that China and host states’ domestic leaders mutually benefit by restricting online digital freedom.

OPUS-Rota5: A highly accurate protein side-chain modeling method with 3D-Unet and RotaFormer

Accurate protein side-chain modeling is crucial for protein folding and design. This is particularly true for molecular docking as ligands primarily interact with side chains. In this study, we introduce a two-stage side-chain modeling approach called OPUS-Rota5. It leverages a modified 3D-Unet to capture the local environmental features, including ligand information of each residue, and then employs the RotaFormer module to aggregate various types of features. Evaluation on three test sets, including recently released targets from CAMEO and CASP15, shows that OPUS-Rota5 significantly outperforms some other leading side-chain modeling methods. We also employ OPUS-Rota5 to refine the side chains of 25G protein-coupled receptor targets predicted by AlphaFold2 and achieve a significantly improved success rate in a subsequent "back" docking of their natural ligands. Therefore, OPUS-Rota5 is a useful and effective tool for molecular docking, particularly for targets with relatively accurate predicted backbones but not side chains such as high-homology targets.

Does multiple borrowing and COVID-19 pandemic matter in a microfinance contract?

PurposeTo improve the loan repayment performance (LRP) of microfinance banks (MFBs) in Pakistan, this study aims to look at the direct impact of multiple borrowing (MB) on LRP and client-business performance (CBP), as well as the direct impact of CBP on LRP. The moderating function of pandemic factors in the relationship between MB and CBP, as well as the mediating effect of CBP in the association between MB and LRP, was also investigated in this study.Design/methodology/approachA questionnaire was used to obtain data from 531 lower-level workers of microfinance institutions (MFIs) for the study. The respondents were chosen using stratified sampling, which divided the target population into four influential groups: lending officers in agriculture, lending officers in businesses, lending officers in gold loans and lending officers in salary loans. In this study, a two-stage structural equation modeling approach was used, including a measurement model (outer model) and a structural model (inner model). The validity and reliability of the questionnaire were investigated using the measurement model (outer model), whereas PLS-SEM bootstrapping was performed to test the hypothesis and find the relationship among different underpinning constructs by using the structural model (inner model).FindingsThe outcomes of this study demonstrate that MB has a direct impact on CBP, and that CBP has a direct impact on LRP. MB, on the contrary, had no direct and significant impact on LRP in this study. The idea that CBP mediates the relationship between MB and LRP, as well as the moderating effect of pandemic factors on the relationship between MB and CBP, is supported by this research.Originality/valueUntil now, the influence of MB on LRP via the mediating role of CBP and the moderating role of a pandemic factor in the setting of Pakistani MFBs has received little attention. During the COVID-19 pandemic, this research also aids MFBs in better understanding MB and its impact on LRP. Furthermore, based on the findings of this study, Pakistani MFIs can enhance their LRP by implementing new lending regulations, particularly with reference to MB and the COVID-19 pandemic.

Lung cancer survival prognosis using a two-stage modeling approach

Lung cancer survival prognosis using a two-stage modeling approach

Kepemimpinan Melayani dan Komitmen Afektif pada Perubahan di Perguruan Tinggi Indonesia: Model Mediasi Kepemilikan Psikologikal

Based on self-determination theory, this study identified the effect of servant leadership on the affective commitment to change of lecturers in Indonesian. This study empirically examined the specificity of the servant leadership construct when compared with other positive leadership constructs. The mediation effect of psychological ownership of lecturers was also examined. A total of 321 responses were analyzed using a two-stage structural equation modeling (SEM) approach to confirm factors and test the study hypotheses. The results showed that the level of servant leadership had a significant effect on psychological ownership, and servant leadership had a significant indirect effect on affective commitment to change only through psychological ownership of the lecturers. Research limitations and implications are discussed and future directions of research are suggested

The effects of neurodiversity on cognitive attributes of entrepreneurs

PurposeThis study investigates how attention deficit/hyperactivity disorder (ADHD) in entrepreneurs functions through coping schema to affect entrepreneurship-related cognitions. It is proposed that the resource-induced coping heuristic (RICH) bridges the conceptual gap between pathological cognitive executive control/reward attributes and cognitive resources, specifically entrepreneurial alertness, cognitive adaptability and entrepreneurial intent.Design/methodology/approachWith data from 581 entrepreneurs, this study utilizes partial least squares structural equation modeling for analysis. Additionally, a two-stage hierarchical component modeling approach was used to estimate latent variable scores for higher-order constructs.FindingsFindings indicate the RICH mediates the relationships ADHD has with alertness, cognitive adaptability and entrepreneurial intent.Originality/valueThe RICH is introduced as a mechanism to explain how ADHD indirectly influences entrepreneurial alertness, cognitive adaptability and entrepreneurial intent.

Promoting Sustainable Consumption: The Roles of Consumers’ Domain-Specific Environmental Knowledge and Personality Traits

Environmental sustainability, a pivotal facet of sustainable development, is explicitly emphasized and advocated. The textile and apparel (T&amp;A) industry, which is resource-intensive, faces challenges in transitioning toward an ecologically friendly market. The shift toward sustainability is causing significant changes in consumer lifestyles and cultural practices, resulting in increased uncertainty in pro-environmental behaviors. This study adapts the environmentally responsible behavior (ERB) model to investigate how consumers’ subjective and objective domain-specific environmental knowledge (SUEK and OBEK) and personality traits—including personal environmental responsibility (PER), eco-centric and anthropocentric environmental beliefs (EEBs and AEBs), and internal and external environmental loci of control (IN-ELOC and EX-ELOC)—influence sustainable consumption intention (SCI) for T&amp;A products. The data from 212 completed online survey questionnaires were analyzed using a two-stage partial least squares structural equation modeling (PLS-SEM) approach. The findings reveal that participants’ SUEK related to the T&amp;A industry, and their PER and EEBs positively influence their SCI for T&amp;A products. This research contributes to the literature on consumer SCI in the T&amp;A industry and adds value to the existing ERB model, providing insights for brands, retailers, educators, policymakers, and stakeholders striving for a more sustainable industry.

How do firm social practices produce social impacts? Investigating the role of social and long-term orientation

Sustainability practices are expected to yield positive outcomes for both firms and the community. The literature reviewing the relationship between sustainability practices and performance has mainly been concerned with environmental issues and financial outcomes. However, there are few empirical studies that deal with the social dimension of sustainability. This article seeks to address these gaps and contribute to social sustainability studies by suggesting a positive correlation between firms' social sustainability practices – towards employees and community - and social performance, highlighting the role of social and long-term orientation as mediators. Social identity theory and practice-based view are employed to explain these relational mechanisms. Data were collected through a survey of Italian firms in the manufacturing and trade sectors. A two-stage structural equation modeling approach was followed to test the proposed mediation model.The results reveal that when practices are supported by a strong orientation, it triggers the process of social identification, thereby enhancing the firm's social outcomes. From a practice-based view, orientation can explain the performance differences among firms. To succeed in enhancing social impacts, managers and policymakers should prioritise promoting the cultural aspects of sustainability.

Modeling car dependency and policies towards sustainable mobility: A system dynamics approach

Despite decades of research on urban transportation, few researchers apply a holistic understanding of the urban transport mechanism. To address this gap, in this paper we employed a two-stage modeling approach using a system dynamics method and theory of path dependence. First, an exploratory model presents the structure, including feedback loops and underlying mechanisms of the evolution of car dependency. The model explores loop dominance, and implicit goals determine whether it is a car-dependent or transit-oriented and active modes-based transport system. Car dependence arises from the dominance of road-based feedback loops for commuters to achieve their transportation goals. Second, a revised policy structure reveals that shifting the loop dominance towards transit-oriented and active modes-based transportation system is possible when commuters achieve their goals through transit and active infrastructure-based feedback loops. The scale and timing of policy leverages determine the thresholds for the loop dominance shift.

Impact of green factors on undergraduate students’ green behavioral intentions: A hybrid two-stage modeling approach

Consumers' unsustainable behavior contributes to environmental degradation and impedes sustainability. Using green products is one way to reduce this effect and promote environmental growth. Therefore, this study aims to investigate the green factors that impact young customers' behavioral intentions regarding green products. For this purpose, the researcher designed a research model based on the expanded theory of planned behavior (TPB). The study adopted a two-stage, hybrid model using partial least square-structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) to evaluate and validate the results. A sample of 382 undergraduate students was gathered using a convenience sampling approach. The results reveal that green TPB perception (GTP), green price sensitivity (GPS), green product trust (GPT), and green product value (GPV) are significantly and positively correlated with green behavioral intention (GBI). This study's main contribution is adding a brand-new higher-order construct, GTP, in the environmental and marketing literature and validating its effect on GBI. Again, environmental awareness moderates the association between GPS and GBI. Furthermore, the NCA's findings indicated that these variables are necessary to various degrees for students' GBI. Decision-makers may use the study's insights to create successful regulations to better understand young customers and develop appropriate green initiatives for sustainable development.

Cost-Efficient Two-Level Modeling of Microwave Passives Using Feature-Based Surrogates and Domain Confinement

A variety of surrogate modeling techniques has been utilized in high-frequency design over the last two decades. Yet, the curse of dimensionality still poses a serious challenge in setting up reliable design-ready surrogates of modern microwave components. The difficulty of the modeling task is only aggravated by nonlinearity of circuit responses. Consequently, constructing a practically usable surrogate model, valid across extended ranges of material, geometry, and operational parameters, is far from easy. As a matter of fact, conventional modeling techniques are merely capable of building models for microwave structures featuring a relatively small number of designable parameters within reduced ranges thereof. One possible way of mitigating these obstacles may be the employment of the recently proposed two-stage performance-driven modeling approach. Therein, the surrogate model domain is narrowed down to the section of the space where the vectors of adequate quality are located, thereby permitting significantly reducing the cost of acquiring the training data. Seeking even further cost reduction, this work introduces a novel modeling framework, which exploits problem-specific knowledge extracted from the circuit responses to achieve substantial cost-savings of training data acquisition. In our methodology, the modeling procedure targets response features instead of the complete responses. The response features are the characteristic locations of the circuit response, such as relevant minima or maxima over selected frequency bands. The dependency of the coordinates of the said features on circuit dimensions is considerably less nonlinear than is observed for the complete characteristics, which enables sizable reduction of the data acquisition cost. Numerical validation of our procedure involving three microwave structures corroborates its remarkable efficiency, which allows for setting design-ready surrogates using only a handful of samples.

Cell phone-related driver distraction: Habits predict behavior over and above the theory of planned behavior variables

IntroductionHabits have often been overlooked in studies investigating cell phone-related driver distractions. This paper examines the association between habits and cell phone-related driver distractions within a mediation model based on the Theory of Planned Behavior (TPB). Additionally, it explores potential differences in behaviors across urban and rural driving environments and between males and females. MethodWe conducted an online survey in China with 1,016 respondents, measuring attitudes, subjective norms, perceived behavioral control, self-reported behavior, and habits associated with cell phone use while driving. ResultsData was analyzed using a two-stage structural equation modeling approach. Results indicate that the measurement model provided a good fit to the data and was invariant across urban and rural driving environments, as well as across genders. The latent path model investigating mediation also demonstrated a good fit and revealed that TPB variables (attitudes, subjective norms, and perceived behavioral control) partially mediated the relationship between cell phone-related habits and cell phone use while driving. The structural model was invariant across driving environments but not across genders, for which the extent of the differences were limited. Moreover, habits were strongly associated with subjective norms and perceived behavioral control, emerging as the strongest predictor of cell phone-related distractions. ConclusionsThe findings suggest that habits should be considered in research on phone-related distracted driving behaviors and in the development of intervention designs.

Retracted: Multi-objective grasshopper optimization algorithm for optimal energy scheduling by considering heat as integrated demand response

Due to the challenges associated with grid management, it is crucial to develop energy management systems that consider optimal resource performance within an integrated system and enable control of power exchange with the grid. Simultaneously, addressing the demand for multiple energy sources is a critical step towards achieving an economically and efficiently integrated energy system (IES), which plays a pivotal role in promoting sustainable IES development. Building upon these considerations, this article proposes a two-objective optimization model that incorporates the concept of demand response and integrated energy systems, while considering comprehensive economic and energy efficiency benefits. To ensure realism, a range of linear and nonlinear constraints are employed. To address such a problem, a two-stage modeling and solution approach is proposed. Initially, flexible electrical and thermal loads are combined to establish a mechanism for effectively responding to system behavior. Subsequently, a multi-objective model is presented to enhance energy generation and transfer efficiency, taking into account both demand response and economic aspects. Given the distinct properties of these objectives, a multi-objective grasshopper optimization algorithm (GOA) is developed. The Pareto concept is utilized to enhance search performance, and fuzzy theory is employed to select the optimal solution from the set of solutions. Finally, the proposed model and algorithm are evaluated under various operating conditions on the studied system. The results demonstrate that this model effectively improves economic aspects, enhances efficiency, and reduces pollutant emissions to an acceptable level.