Dimensions Of Variables Research Articles

Consumer style inventory (CSI) re-examined: The case of millennial decision-making style in a developing market

This study investigates the buying styles of young consumers, especially the millennials—Gen Y, and Gen Z whose idiosyncrasies and consumption peculiarities are quite different from the older generations. Besides Sproles and Kendall’s eight (8) consumer-style inventory dimensions, this study presents new dimensions and develops six constructs that define young consumers’ decision-style inventory in a developing market. The study population consisted of all younger consumers—Gen Y, and Gen Z in Lagos State, Nigeria. One hundred and twenty-five (125) respondents were selected randomly across all 20 Local Governments in Lagos State, Nigeria. Factor analyses through varimax rotation, latent root criterion (eigenvalue = 1), screen plot test and the percentage of variance were conducted to determine the significant factors to retain among the variables. The findings clearly showed that newly developed CSI constructs in this study (sexiness, trendiness, global branding, smartness, socialisation and entertainment) were strong and significant among young consumers’ decision-making styles. The six (6) constructs developed showed that the younger consumers’ consumption styles are evolving, becoming sophisticated and relatively dynamic, hence the reliance on Sproles and Kendall’s dimensions to measure the younger consumers’ consumption decision styles will be inadequate in business/behaviour strategy development. The dimensions of entertainment, sexy, social, trendy, smartness and global branding variables are mostly underpinned and dominate considerations in purchase decision styles and behaviours among young consumers.

Principal component analysis revisited: fast multitrait genetic evaluations with smooth convergence.

A cornerstone in breeding and population genetics is the genetic evaluation procedure, needed to make important decisions on population management. Multivariate mixed model analysis, in which many traits are considered jointly, utilizes genetic and environmental correlations between traits to improve the accuracy. However, the number of parameters in the multitrait model grows exponentially with the number of traits which reduces its scalability. Here, we suggest using principal component analysis to reduce the dimensions of the response variables, and then using the computed principal components as separate responses in the genetic evaluation analysis. As principal components are orthogonal to each other so that phenotypic covariance is abscent between principal components, a full multivariate analysis can be approximated by separate univariate analyses instead which should speed up computations considerably. We compared the approach to both traditional multivariate analysis and factor analytic approach in terms of computational requirement and rank lists according to predicted genetic merit on two forest tree datasets with 22 and 27 measured traits, respectively. Obtained rank lists of the top 50 individuals were in good agreement. Interestingly, the required computational time of the approach only took a few seconds without convergence issues, unlike the traditional approach which required considerably more time to run (7 and 10 h, respectively). The factor analytic approach took approximately 5-10 min. Our approach can easily handle missing data and can be used with all available linear mixed effect model softwares as it does not require any specific implementation. The approach can help to mitigate difficulties with multitrait genetic analysis in both breeding and wild populations.

Pengaruh Emotional Labour dan Reward terhadap Performa Kerja Karyawan Divisi Pemasaran PT MI

Purpose: This study aims to deepen the understanding of the influence of emotional labor ability and rewards on employee performance, with a case study focusing on PT MI. Methodology: This study used a quantitative approach by collecting data using a questionnaire from 30 employees of the marketing division of PT MI. Statistical analyses were performed to examine employee characteristics, the validity and reliability of the questionnaire, the dimensions of variables, and the links between variables, mainly using multiple linear regression. Results: The results indicate that rewards significantly affect job performance, while emotional labor does not have a significant impact. Both independent variables simultaneously and significantly influence job performance. The coefficient of determination of 62.3% indicates that emotional labor and rewards can explain 62.3% of the variability in job performance, while the remaining variability is explained by other factors. Limitations: We face a limitation in collecting a higher number of respondents due to the limited number of employees in the marketing division of PT MI. Contribution: This study found that financial and non-financial rewards significantly enhance job performance through intrinsic and extrinsic motivation and psychological contracts between employees and the organization. In contrast, the lack of a significant impact of emotional labor may be due to individual differences in managing emotions, job characteristics, and other dominant factors, such as technical skills and intrinsic motivation. These findings underscore the importance of rewards in improving employee performance and the need for further research on the role of emotional labor in various contexts.

Investigation of Chinese College Students’ Perceptions, Needs, and Self-Assessment of Spoken English Competency

Spoken English Competency (SEC) remains a critical weakness in English Proficiency (EP) of Chinese college students, particularly when compared with their English competency in writing and reading. Despite years of English learning since elementary school, Chinese college students often struggle to master SEC. Although previous studies have explored Chinese students’ SEC from various perspectives, there is still a significant knowledge gap, especially from students’ perspectives. This study aims to analyze students’ Perceptions and Needs of Spoken English (PNSE) and their Self-Assessment of Spoken English Competency (SA-SEC) with the language output evaluation indicators (including three dimensions: complexity, accuracy, and fluency [CAF]). The study analyzed 2,677 (male = 1,340, female = 1,337) online questionnaires from college students across four English proficiency distinct groups. There are four identified aspects of PNSE (i.e., oral importance, difficulty, purpose, and improvement willingness). Similarly, SA-SEC was analyzed with three dimensions of CAF, namely complexity, accuracy, fluency, and nominal variables like pause type. The findings revealed that gender and EP significantly influenced PNSE and SA-SEC, and correlations were observed between paired variables of PNSE and dimensions of CAF in the SA-SEC section. Notably, complexity was identified as the weakest dimension of CAF for most students. Future studies are imperative to further explore the intricate relationship between PNSE and SA-SEC, and how factors such as pause type influence students’ spoken English performance.

A three-stage prediction model for firm default risk: An integration of text sentiment analysis

Predicting firm default risk is vital for financial institutions to avert significant economic losses, making the enhancement of its prediction precision both imperative and intricate. This research introduces a three-stage prediction model, including association rule algorithm (ARA), support vector machine (SVM) and modified particle swarm optimization algorithm (MPSO). Features selected by ARA are used as inputs for SVM, and penalty parameter and kernel parameter of SVM is optimized by MPSO that uses the adaptive inertia weight. The importance of text sentiment variables are emphasized to predict firm default risk. In the first stage, feature selection seeks to curtail the dimensions of both financial and non-financial variables. The empirical findings validate the efficacy of the ARA, revealing a strong correlation between text sentiment and default risk. The subsequent two stages deploy the SVM, refined by the MPSO, to predict the default risk. Compared with renowned models, the proposed model displays superior prediction precision and a reduced computational overhead. This research furnishes a potent instrument for regulators and firms alike, aiding in mitigating prospective default risks and forestalling broader economic upheavals.

Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling.

To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network psychometric models to assess the dimensionality structure of the data. The study applied Bayesian conjugate or Jeffreys' priors to estimate the graphical structure and then used the Louvain community detection algorithm to partition and identify groups of nodes, which allowed the detection of the multi- and unidimensional factor structures. Monte Carlo simulations suggested that the two alternative Bayesian estimation algorithms had comparable or better performance when compared with the GLASSO-based EGA and conventional parallel analysis (PA). When estimating the multidimensional factor structure, the analytically based method (i.e., EGA.analytical) showed the best balance between accuracy and mean biased/absolute errors, with the highest accuracy tied with EGA but with the smallest errors. The sampling-based approach (EGA.sampling) yielded higher accuracy and smaller errors than PA; lower accuracy but also lower errors than EGA. Techniques from the two algorithms had more stable performance than EGA and PA across different data conditions. When estimating the unidimensional structure, the PA technique performed the best, followed closely by EGA, and then EGA.analytical and EGA.sampling. Furthermore, the study explored four full Bayesian techniques to assess dimensionality in network psychometrics. The results demonstrated superior performance when using Bayesian hypothesis testing or deriving posterior samples of graph structures under small sample sizes. The study recommends using the EGA.analytical technique as an alternative tool for assessing dimensionality and advocates for the usefulness of the EGA.sampling method as a valuable alternate technique. The findings also indicated encouraging results for extending the regularization-based network modeling EGA method to the Bayesian framework and discussed future directions in this line of work. The study illustrated the practical application of the techniques to two empirical examples in R.

Pengaruh Pemanfaatan Fitur Virtual Try-On Berbasis Augmented Reality pada Aplikasi Tiktok dalam Pemilihan Produk Cushion Oleh Mahasiswa di Fakultas Pariwisata dan Perhotelan Universitas Negeri Padang

This research is motivated by problems. The digital era has had a significant impact on social, economic and cultural aspects, especially in the field of information technology. Information technology is an ever-growing field that enables the exchange and sharing of information. The application of digital marketing is currently increasingly being carried out considering that industrial competition, especially in the e-commerce sector, is getting tighter. As one of the largest e-commerce in Indonesia, Tiktok has adopted the use of augmented reality virtual try on technology as one form of its digital marketing efforts to create a more enjoyable user experience. This research was conducted with the aim of analyzing the effect of using the virtual try-on feature based on augmented reality on the Tiktok application by Generation Z. This research took a study of the virtual try-on feature found on Indonesian Tiktok e-commerce for beauty products. This research used a quantitative approach through causal associative techniques on 264 respondents who were interested in Cushion products. Data analysis in this research was obtained using PLS-SEM which showed the results that the dimensions of the augmented reality variables had a positive effect on product selection at Cusion.

Is Self-Reported Obstructive Sleep Apnea Associated with Cardiac Distress? A Network Analysis

Abstract Introduction: The relationship between obstructive sleep apnea (OSA), obesity, various metabolic variables, and psychosocial outcomes is complex. No studies have examined the association between these predictors and disease-specific distress related to heart disease (cardiac distress). We aimed to study the association between OSA and cardiac distress using a network analysis framework. Methods: This secondary analysis of an observational cross-sectional study conducted in 2021 consisted of 405 hospital- and community-sourced adults from Australia and the United States who reported an acute coronary event (such as a myocardial infarction, or procedures such as coronary artery bypass graft surgery, or percutaneous coronary intervention) in the previous 12 months. Participants were surveyed in relation to sociodemographic variables, clinical risk factors, comorbidities (including time since event, OSA, obesity, diabetes, hypertension, and hyperlipidemia), and cardiac distress (reported by the Cardiac Distress Inventory Short-Form). These data were subjected to bootstrapped exploratory graph analysis (EGA), which identifies the dimensions of variables that cluster together. Variables that contributed to the EGA dimensions were used to predict cardiac distress using multivariable logistic regression. Results: Three distinct dimensions were identified by the EGA: Dimension 1 – clinical risk factors and conditions including OSA, Dimension 2 – variables related to the heart event, and Dimension 3 – variables closely related to cardiac distress. For Dimension 1, only OSA was a significant predictor of cardiac distress in the fully adjusted model (adjusted odds ratio = 2.08, 95% confidence interval = 1.02–4.25, P = 0.044). Further analysis indicated that OSA was associated with physical challenges and changes in roles and relationships. Conclusions: This study identified that self-reported OSA is associated with cardiac distress, particularly distress that was associated with physical challenges and changes to roles and relationships. These findings imply that OSA could potentially increase stress in a relationship; however, distress was only assessed from the perspective of the participant with OSA in this study. EGA is a useful method for describing complex associations between diverse predictor variables such as OSA and cardiac distress. Owing to the self-reported aspect of the data, further investigation to confirm study outcomes is warranted.

Analisis Bauran Pemasaran dalam Upaya Meningkatkan Partisipasi Anggota di Koperasi Karyawan Sejahtera Universitas PGRI, Yogyakarta

The existence of cooperatives as the center of economic activity for most Indonesians has begun to be displaced by competitors such as more modern shops and mini markets. Therefore, improving the quality of service and member participation in the cooperative is important in improving its business. Researchers conducted research using qualitative methods and used descriptive statistics to determine the marketing mix and member participation in the Sejahtera Employees Cooperative, PGRI University, Yogyakarta. The sampling technique used was simple random sampling with a total of 30 respondents. There are twenty-six indicators which fall into ten dimensions and two variables. In conveying messages about their products to consumers, all dimensions of the marketing mix variables and member participation in the Sejahtera Employees Cooperative, PGRI University, Yogyakarta fall into the very high category. The dimension with the best score is the place with a score of 4.98. Meanwhile, the dimension with the lowest score is processed with a score of 4.57. This shows that the Prosperous Employee Cooperative, PGRI University, Yogyakarta has implemented the 7P marketing mix and member participation well.

Stochastic First-Order Algorithms for Constrained Distributionally Robust Optimization

We consider distributionally robust optimization (DRO) problems, reformulated as distributionally robust feasibility (DRF) problems, with multiple expectation constraints. We propose a generic stochastic first-order meta-algorithm, where the decision variables and uncertain distribution parameters are each updated separately by applying stochastic first-order methods. We then specialize our results to the case of using two specific versions of stochastic mirror descent (SMD): (i) a novel approximate version of SMD to update the decision variables, and (ii) the bandit mirror descent method to update the distribution parameters in the case of [Formula: see text]-divergence sets. For this specialization, we demonstrate that the total number of iterations is independent of the dimensions of the decision variables and distribution parameters. Moreover, the cost per iteration to update both sets of variables is nearly independent of the dimension of the distribution parameters, allowing for high-dimensional ambiguity sets. Furthermore, we show that the total number of iterations of our algorithm has a logarithmic dependence on the number of constraints. Experiments on logistic regression with fairness constraints, personalized parameter selection in a social network, and the multi-item newsvendor problem verify the theoretical results and show the usefulness of the algorithm, in particular when the dimension of the distribution parameters is large. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms—Continuous. Funding: This work was supported by the National Science Foundation [Grant 2112533]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2023.0167 .

Servitization and digital integration of manufacturing enterprises in China: Performance and strategic paths

The integration of digitalization and servitization has become a significant trend in transforming the manufacturing industry due to digital intelligence technology. This paper examines the impact of the integration of digitalization and servitization on the performance of manufacturing companies and how small-scale enterprises can promote digital transformation leading to servitization. The study involved surveying 331 manufacturing companies in China using a seven-point Likert scale questionnaire. Measurement scales were validated using confirmatory factor analysis and discriminant validity tests. Mediation analysis assessed digitalization’s impact on servitization and firm performance. The study’s findings emphasize the significant impact of digitalization and servitization on enterprises’ performance. Digitalization plays a crucial role in mediating this relationship. The study highlights three critical dimensions of digital variables, including digital technology, digital labor, and digital relationship resources, essential in enabling effective servitization. Manufacturing enterprises generally prefer aligning their technology investments and organizational changes within the digitalization framework to implement servitization successfully. The study suggests two integration strategies, namely conservative and aggressive. The finding emphasizes that the convergence of digitalization and servitization leads to a new manufacturing production mode called digital servitization.

Perception of University Students on The Use of Artificial Intelligence (AI) Tools For The Development of Autonomous Learning

Objective: To explore the perception of university students on the use of Artificial Intelligence (AI) tools for the development of autonomous learning. Theoretical Framework: The research is based on Technological Acceptance Theory and Constructivism, focusing on the perception of AI in autonomous learning of university students. Method: Quantitative approach with a descriptive scope, the sample consisted of 665 students enrolled in the Faculty of Education Sciences and Languages (FCEI) of the Peninsula de Santa Elena State University (UPSE)-Ecuador; in the collection of information, the Questionnaire of Perception on the Use of Artificial Intelligence for Autonomous Learning was designed based on 4 dimensions of both variables, and the statistical program SPSS version 29 was used for data processing. Results and Discussion: The results indicate that students show a favorable perception towards the use of AI tools for the autonomous learning process, however, although AI is recognized as a potential tool in university environments, there are still challenges to be overcome. Research Implications: The study has practical implications for strengthening in students the digital competencies needed to effectively use AI tools in their autonomous learning. Originality/Value: The research provides data on the perception of AI tools among university students, offering a starting point for future technology integration strategies in universities.

Improving disentanglement in variational auto-encoders via feature imbalance-informed dimension weighting

Using Variational Auto-Encoder (VAE) to learn disentangled representation holds great promise. But there is a feature imbalance in the learning process of VAEs, and the model usually concentrates on the learning of some dimensions at the expense of others. Instead of attempting to rectify it, we exploit feature imbalance to propose a Dimension Weighting method that can boost the disentanglement effect of VAE-based models. In order to conduct disentanglement learning under a fixed size of the latent space, the intrinsic dimension of raw data is estimated by a Dimension Number Estimator and set as the latent space size. Then, leveraging the feature imbalance, a Dimension Importance Evaluator is constructed to separate each dimension in the latent variable into important, unimportant, and general dimensions. By exerting different learning pressures on specific dimensions, we further optimize the variational lower bound of model and retrain it, thus promoting the disentanglement of important dimensions. The experiments on four benchmark datasets show that the Dimension Weighting can further improve the disentangling effect without compromising model performance, and in approximately 80% of cases, the results in disentangling metric evaluation experiments achieve better disentangling scores than the original models. This reveals that not all dimensions of the latent variables are equally influential. By focusing on the crucial dimensions within the latent representation, the model can achieve better performance.

Manifold-guided multi-objective gradient algorithm combined with adjoint method for supersonic aircraft shape design

The aerodynamic design of supersonic aircraft necessitates the comprehensive consideration of multiple conflicting design objectives and multi-disciplinary characteristics. However, the efficiency of heuristic algorithms in solving multi-objective aerodynamic problems significantly decreases with the increase in the number of design variables and objectives. Although gradient-based adjoint optimization methods can eliminate the constraints of design variables on aerodynamic optimization and have high optimization efficiency, they are prone to getting stuck in local optima and are difficult to apply in multi-objective optimization. To overcome these shortcomings, a manifold-guided multi-objective gradient-based algorithm (MG-MOGBA) is proposed. This algorithm enhances the optimization efficiency of the algorithm by coupling the multi-objective gradient operator with the predicted Pareto front (PF) manifold structure to guide the population search direction and reduce the search space. A multi-objective aerodynamic optimization framework based on the discrete adjoint method is developed to automate the aerodynamic optimization process. Test functions and airfoil optimization results confirm that the proposed algorithm achieves superior results with fewer iterations and is more efficient than heuristic algorithms. The four-objective supersonic civil aircraft example demonstrates that the algorithm can effectively handle high-dimensional multi-objective aerodynamic optimization problems. A potential equilibrium solution increased the cruise factor by 17.5% and 12.8% under supersonic and transonic conditions respectively, reduced the maximum overpressure of the sonic boom by 25.1%, and decreased the root bending moment by 20.3%. Overall, MG-MOGBA can efficiently solve high-dimensional multi-objective aerodynamic optimization problems and has good scalability for the dimensions of design objectives and variables.

Evolving demographics: a dynamic clustering approach to analyze residential segregation in Berlin

This paper examines the phenomenon of residential segregation in Berlin over time using a dynamic clustering analysis approach. Previous research has examined the phenomenon of residential segregation in Berlin at a high spatial and temporal aggregation and statically, i.e. not over time. We propose a methodology to investigate the existence of clusters of residential areas according to migration background, age group, gender, and socio-economic dimension over time. To this end, we have developed a sequential mixed methods approach that includes a multivariate kernel density estimation technique to estimate the density of subpopulations and a dynamic cluster analysis to discover spatial patterns of residential segregation over time (2009-2020). The dynamic analysis shows the emergence of clusters on the dimensions of migration background, age group, gender and socio-economic variables. We also identified a structural change in 2015, resulting in a new cluster in Berlin that reflects the changing distribution of subpopulations with a particular migratory background. Finally, we discuss the findings of this study with previous research and suggest possibilities for policy applications and future research using a dynamic clustering approach for analyzing changes in residential segregation at the city level.

Data-driven surrogate model for aerodynamic design using separable shape tensor method

In the context of increasing dimensionality of design variables and the complexity of constraints, the efficacy of Surrogate-Based Optimization (SBO) is limited. The traditional linear and nonlinear dimensionality reduction algorithms are mainly to decompose the mathematical matrix composed of design variables or objective functions in various forms, the smoothness of the design space cannot be guaranteed in the process, and additional constraint functions need to be added in the optimization, which increases the calculation cost. This study presents a new parameterization method to improve both problems of SBO. The new parameterization is addressed by decoupling affine transformations (dilation, rotation, shearing, and translation) within the Grassmannian submanifold, which enables a separate representation of the physical information of the airfoil in a high-dimensional space. Building upon this, Principal Geodesic Analysis (PGA) is employed to achieve geometric control, compress the design space, reduce the number of design variables, reduce the dimensions of design variables and enhance predictive performance during the surrogate optimization process. For comparison, a dimensionality reduction space is defined using 95% of the energy, and RAE 2822 for transonic conditions are used as demonstrations. This method significantly enhances the optimization efficiency of the surrogate model while effectively enabling geometric constraints. In three-dimensional problems, it enables simultaneous design of planar shapes for various components of the aircraft and high-order perturbation deformations. Optimization was applied to the ONERA M6 wing, achieving a lift-drag ratio of 18.09, representing a 27.25% improvement compared to the baseline configuration. In comparison to conventional surrogate model optimization methods, which only achieved a 17.97% improvement, this approach demonstrates its superiority.

Mobile Marketing and Consumer’s Purchase Decisions among Students of Tertiary Institutions in Akwa Ibom State

This study was carried out to assess the relationship between mobile marketing and purchase decisions of students in the tertiary institutions. The researchers adopted a survey research technique in the study. The population size was students of three tertiary institutions in Akwa Ibom State and a sample size of 366 employees was studied. The mobile marketing variables (independent variables) considered in the study were Short Message Service, Mobile Website and Social Media Marketing. The dependent variable was Consumer’s Purchase Decision. Research questions were answered using frequency, mean and standard deviation. The hypotheses stated were tested using Spearman Correlation Coefficient at a 5% level of significance. From the data analysis, the major findings revealed a positive and significant relationship between the dimensions of mobile marketing variables (Short Message Service, Mobile Website and Social Media Marketing) and consumer’s purchase decision of Nigerian students in the tertiary institutions. From the findings, it was concluded that there was a positive and significant relationship between mobile marketing and consumer’s purchase decision of Nigerian students in Akwa Ibom State. In line with this, it was recommended that companies should endeavor to influence their customers’ purchase decisions by making their products accessible through mobile devices platforms to interact with their customers and keep them informed of new product features.

The Real-Time Dynamic Prediction of Optimal Taxi Cruising Area Based on Deep Learning

A real-time, effective, and dynamic taxi cruising recommendation strategy is essential to solving the problem of taxi cruising passenger difficulty and urban road traffic congestion. This study focuses on two aspects of the real-time accessible range and pick-up ratio (PR) and proposes a real-time dynamic identification method for taxi optimal cruise-seeking area. Firstly, based on the cumulative opportunity method, a univariate temporal convolutional network (UTCN) accessible range dynamic prediction model is proposed to predict the real-time accessible range of taxis. Secondly, based on the gradient boosting decision tree (GBDT) model, the influencing factors with a high correlation with the PR are selected from the four dimensions of traffic characteristics, environmental meteorology, and time and space variables. Then, a multivariate univariate temporal convolutional network (MTCN) global grid PR prediction model is constructed, and the optimal taxi cruising area is identified based on the maximum PR. The results show that the taxi accessible range and PR of the same grid in different periods change with time, and based on the model comparison, the accessible range and PR prediction results of UTCN and MTCN algorithms in different periods are the best to identify the optimal cruising area of taxis in different periods. The main contribution of this study is that the proposed optimal cruising area prediction model has timeliness, accessibility, and dynamics. It can not only improve the probability of taxis receiving passengers and avoid taxis cruising aimlessly, but also solve the shortage of taxis in hotspots, thus shortening the waiting time of passengers. This provides a scientific basis for improving taxi cruising efficiency and the government’s formulation of taxi operation management policies, which can effectively promote the sustainable development of urban traffic.

The Influence of Using Social Media Instagram @Fanirahm to Fulfill the Information Needs of Beauty Products for Generation Z in Bandung City

The existence of technological developments in this digital era brings people into easy life with activities without any limitations of space and time. Social media has now become a new medium for people to communicate, making the use of social media becomes part of people's lives. It can be seen by the data presenting the large number of internet and social media users. Through this high usage, beauty content creators have emerged, one of whom is Fani Rahmawati. She is a source of information about beauty products. This research aims to determine the magnitude of the influence of the use of social media Instagram @fanirahm on fulfilling the information needs for beauty products among Generation Z in Bandung City. The indicators tested in the research employed social media use variables with the dimensions of context, communication, collaboration, connection and information need variables with the dimensions current need approach, daily need approach, exhaustive need approach, and catching-up need approach. The research method used quantitative with descriptive research type involving the Generation Z population in Bandung City. Sampling was carried out using a random sampling technique, with a sample size of 400 people through distributing questionnaires. The research results indicate that the use of social media Instagram @fanirahm has a positive and significant influence of 86,3% on fulfilling the information needs of Generation Z in Bandung City. For future researchers, they are able to conduct research on the same topic but using different research methods.

The Impact of Intellectual Capital on Improving the Quality of Educational Services: An Exploratory Study of the Opinions of a Sample of Administrative Leaders in Private Colleges: Al-Mammon University College and Bilad Al-Rafidian University College

This research aims to demonstrate the role, relationship, and impact of intellectual capital on the quality of educational services. The measurement of these two variables relied on the dimensions of each, where the dimensions of intellectual capital were defined as human capital, structural capital, and relational capital, while the dimensions of the quality of educational services were considered to be resources, curriculum, reliability, and efficiency. A questionnaire containing 42 items was designed to cover the dimensions of both variables, which were distributed among the research sample members in the studied community, represented by the faculties of Al-Mamoun University and Bilad al-Rafidain University College. A sample of 92 individuals holding higher degrees and assigned to leadership positions in the two colleges was selected, including roles such as Deputy Dean, Department Head, Department Coordinator.