ANALISIS TECHNOLOGY ACCEPTANCE MODEL PADA APLIKASI PLATFORM SHOPEE DENGAN PENDEKATAN PARTIAL LEAST SQUARE (STUDI KASUS PADA MAHASISWA UNIVERSITAS DIPONEGORO)

E-commerce refers to business transactions using digital networks such as the internet. Based on the rank on the Appstore and Playstore, Shopee places the first rank. In 2019, Shopee had 56 million visitors. Meanwhile, in the same year, it had 3,225 workers. The imbalance between the number of Shopee visitors and Shopee employees allows users to be disappointed with Shopee's services, but on the other hand, there are also many users who are happy with its services. With both positive and negative responses to the services provided by Shopee, this study analyzes the factors affecting the acceptance of Shopee Apps on students of Universitas Diponegoro Semarang. The analysis was based on the Technology Acceptance Model (TAM). It used the Structural Equation Modeling with the Partial Least Square (SEM-PLS) approach. The study used primary data obtained by distributing questionnaires to students of Universitas Diponegoro. The result showed 28 valid indicators, 5 deal inner models, and 8 significant pathways. All the causality between latent variables contained in the Technology Acceptance Model (TAM) have a positive and significant effect, it's just that the results of integrating trust variables on TAM, namely the latent variable between trust and interest in usage behavior, have no significant effect.

Distilling vector space model scores for the assessment of constructed responses with bifactor Inbuilt Rubric method and latent variables

In this paper, we highlight the importance of distilling the computational assessments of constructed responses to validate the indicators/proxies of constructs/trins using an empirical illustration in automated summary evaluation. We present the validation of the Inbuilt Rubric (IR) method that maps rubrics into vector spaces for concepts’ assessment. Specifically, we improved and validated its scores’ performance using latent variables, a common approach in psychometrics. We also validated a new hierarchical vector space, namely a bifactor IR. 205 Spanish undergraduate students produced 615 summaries of three different texts that were evaluated by human raters and different versions of the IR method using latent semantic analysis (LSA). The computational scores were validated using multiple linear regressions and different latent variable models like CFAs or SEMs. Convergent and discriminant validity was found for the IR scores using human rater scores as validity criteria. While this study was conducted in the Spanish language, the proposed scheme is language-independent and applicable to any language. We highlight four main conclusions: (1) Accurate performance can be observed in topic-detection tasks without hundreds/thousands of pre-scored samples required in supervised models. (2) Convergent/discriminant validity can be improved using measurement models for computational scores as they adjust for measurement errors. (3) Nouns embedded in fragments of instructional text can be an affordable alternative to use the IR method. (4) Hierarchical models, like the bifactor IR, can increase the validity of computational assessments evaluating general and specific knowledge in vector space models. R code is provided to apply the classic and bifactor IR method.

The Role of Conditional Likelihoods in Latent Variable Modeling

In psychometrics, the canonical use of conditional likelihoods is for the Rasch model in measurement. Whilst not disputing the utility of conditional likelihoods in measurement, we examine a broader class of problems in psychometrics that can be addressed via conditional likelihoods. Specifically, we consider cluster-level endogeneity where the standard assumption that observed explanatory variables are independent from latent variables is violated. Here, “cluster” refers to the entity characterized by latent variables or random effects, such as individuals in measurement models or schools in multilevel models and “unit” refers to the elementary entity such as an item in measurement. Cluster-level endogeneity problems can arise in a number of settings, including unobserved confounding of causal effects, measurement error, retrospective sampling, informative cluster sizes, missing data, and heteroskedasticity. Severely inconsistent estimation can result if these challenges are ignored.

Validating second-order model for economic welfare scale: evidence from Nigeria

PurposeThe goal of this paper is to validate the second-order model for the economic welfare scale in the context of violence. This study also aims to assess the relationship between the dimensions of the economic welfare scale’ declining food consumption and loss of income and the overall latent construct and assess the second-order model’s goodness of fit using appropriate fit indices.Design/methodology/approachThe study is cross-sectional with a sample of 600 households from the violent zone, Northwest Nigeria. The data collected was used for confirmatory factor analysis, second-order model evaluation and model fit evaluation.FindingsThe second-order model for the economic welfare scale is valid and reliable; the dimensions significantly affect the formation of the overall construct. The model’s goodness of fit fulfilled the relevant fit indices.Research limitations/implicationsThe study offers researchers and policymakers practical insights into how each dimension influences the latent operational construct. It, therefore, encompasses replication in all the remaining modules.Practical implicationsThe findings offer practical insight to policymakers in designing policies for promoting long-term peace structures and developing mechanisms to assist those who have suffered the greatest economic welfare losses due to violence in Nigeria.Social implicationsThe findings form an essential tool to assess the economic welfare effect in violently affected territories at the micro-level.Originality/valueThe outcomes are ground-breaking by validating the second-order model for the economic welfare scale. And established dimension influences over the overall latent variable.

Linking employee perceptions and employee attitudes through their levels of organizational identification: a field investigation in India

PurposeDespite organizational socialization and support, contemporary managers often perceive employees to be less engaged and attached to their workplace, multiplying their workload with unsolicited vexations and worries. In this connection, the purpose of this paper is to explore and possibly confirm the ameliorative role of organizational identification as a mediator between employees' perceptions of organizational support and justice and their favorable association to their levels of engagement and attenuation of their intentions to quit.Design/methodology/approachSuitable theories such as the social exchange and fairness heuristics theories were examined to select and support the study constructs. Accordingly, the literature was reviewed to formulate the study hypotheses and connect them through a conceptual latent variable model (LVM). Data were collected from 402 full-time managerial executives all over India. The data thus collected were subjected to structural equation modeling (SEM) procedures.FindingsAll the measures used in this study had acceptable reliabilities as indicated by their Cronbach's Alpha values. Based on the SEM procedures all the study hypotheses and one of the competing LVMs labeled as LVM5 was finally accepted.Originality/valueThe distinctive feature of this study is the theoretical compilation of all the study constructs in one LVM and subsequent empirical verification of the same. This study is, perhaps, the first of its kind to examine the implications of such justice-based perceptions of social exchange relations between employees and their organizations in India more so, since it considers support and justice to complement each other as an interactive whole.

Structural Causality Between National Examination Score and The School Accreditation Categories

This study aims to compare and determine the best model to describe the relationship between National Education Standard (NES) and CBNE scores using generalized structured component analysis. Model 1 describes the causal relationship between the NES and CBNE based on the educational theory of the Ministry of National Education and the Ministry of Religion (2010), Model 2 describes the causal relationship between the NES and CBNE based on the educational theory of the Ministry of Education and Culture (2012), and Model 3 describes the causal relationship between the NES and CBNE based on the educational theory of the Ministry of Education and Culture (2017). The results of the structural model evaluation have found that in Model 1, the SI path coefficient to Academic Achievement (PA) is not significant, in Model 2, the SI path coefficient to PA and SPT to SPN is not significant and in Model 3, the SI path coefficient to PA is also not significant. The coefficient of determination of each endogenous latent variable for each model ranges from 0.20 - 0.75. While the resulting Q-square value for all models is more than 0.9 to represent very good predictive relevance. Based on the overall goodness of fit, it is found that Model 3 produces the largest FIT and AFIT values. So it can be said that model 3 is better than other models. This model produces 11 invalid indicator variables, namely points 17, 39, 51, 55, 57, 59, 73, 75, 76, 80, and 108. The study found that National Education Standards that significantly affect academic achievement are graduate competency standards, process standards, and educational assessment standards