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The purpose of this paper is to estimate the Tanzanian shadow economy (SE) from 2003 to 2015 and test the statistical relationships between the SE and its potential causes and indicators.

The econometric analysis is based on a multiple indicators multiple causes (MIMIC) model. To calibrate the SE from the estimates, the authors adopt the value of 55.4 percentage of the SE to official GDP from the literature for the base year 2005.

The SE ranges from 52 to 61 per cent of official GDP and slightly decreases from 2013 to 2015. Increase in inflation, unemployment and government spending were the main drivers of the SE dynamics.

Given the challenges facing estimation of the SE (e.g. small sample size, exogenous estimate to calibrate the model, meaning of the latent variable), quantification of SE should be considered to be rough measures.

To lower the size of the SE, the government needs to keep inflation and unemployment stable over time, to reduce government spending because it creates pressure on tax collection due to the limited tax base.

This is the first study specifically focused on Tanzanian SE based on the MIMIC approach. Existing estimates of Tanzanian SE are calculated by monetary models or apply a common MIMIC specification to the worldwide context.

The purpose of this paper is to examine the effect of gender on pre‐service teachers' computer attitudes.

A total of 157 pre‐service teachers completed a survey questionnaire measuring their responses to four constructs which explain computer attitude. These were administered during the teaching term where participants were attending a technology course. Structural equation modeling, in particular, confirmatory factor analysis and multiple indicators, multiple causes (MIMIC) modeling were used for data analysis.

No statistical significance is found for gender in the four constructs of computer attitude. However, the mean scores for males are higher for three of the constructs. Overall, the data in this study provides evidence to support the notion that computer attitude is a multidimensional construct.

This study contributes to the continuing interests among researchers to study the effect of gender towards the computer. The results of this study did not support others which found significant differences in computer attitudes by gender. This may be due to heavy reliance of computers in many educational institutions for teaching and learning which consequently granted equal access to male and female users. Methodologically, this study had employed MIMIC model as the technique to assess the effect of gender on computer attitude. MIMIC modeling is superior to conventional techniques (e.g. t‐test, ANOVA) because it is capable of analyzing latent and observed indicators.

This paper contributes to the literature concerning the Nigerian informal economy (IE) by estimating its size from 1991 to 2017 and identifying the major causes.

A structural equation approach in the form of the multiple indicators multiple causes (MIMIC) method is used to estimate the size of the Nigerian IE.

The results indicate that vulnerable employment and urban population as a percentage of the total population are the main drivers of the IE in Nigeria. The IE in Nigeria ranges from 38.83% to 57.55% of gross domestic product (GDP).

As a result of the empirical challenges in the estimation of the IE, the estimates of Nigeria's IE are considered to be rough estimates.

The authors calibrated the MIMIC model with the official estimate of the informal sector published by the Nigerian National Bureau of Statistics (NBS). This was an attempt to combine the national accounting approach, to estimate the size of IE, with the MIMIC approach, and to estimate the trend of informality.

The potential for differential functioning of performance assessments across ratings sources has gained recent research interest. This study used multiple-group confirmatory factor analysis (MGCFA) to examine whether measures of task and contextual performance are invariant across both supervisors and subordinates. As an extension, multiple indicators multiple causes modeling (MIMIC) was used to examine potential covariates of task and contextual performance ratings on latent task and contextual performance variability. Consistent with previous research, I found measurement invariance across subordinate- and supervisor ratings. Moreover, MIMIC results showed supervisor and subordinate demographic variables systematically influenced latent task and contextual performance variability despite measurement invariance over these rating sources. Implications for multi-source performance systems are discussed.

Results from international large-scale assessments, such as PISA surveys, suggest that boys do better in math and science, whereas girls do better in reading. How do gender gaps vary across subjects, when estimated simultaneously? Building on the work of Tsai, Smith, and Hauser (2017), we answer this question by applying a multilevel-MIMIC model that enables us to estimate gender gaps in two ways: gender differences in the effects of observed family and school factors on math, science, and reading scores; and the “adjusted” gender gaps in test scores across all three subjects after controlling for observables. We apply the model to 2012 PISA data of students aged 15–16 and enrolled in 9th or 10th grade in three East Asian (Japan, South Korea, and Taiwan) and three Western countries (USA, Germany, and the Czech Republic) that represent both similar and different types of school systems. Our findings indicate that the gender gap in math or science achievement in Western countries, favoring boys, does not necessarily apply to the East Asian countries examined here, while all three East Asian countries exhibit similar features of gender reading gaps in the 10th grade. There is evidence indicating that observed background and school factors impact boys’ and girls’ achievement in a similar way in USA, Japan, Korea, Taiwan, and the Czech Republic, but not in Germany. Overall, gender differences in family and school influences do not account for gender differences in academic achievement in any of the six countries.

This study aims to analyze a conspicuous corpus of literature related to the field of technology-based intensive care research and to develop an architecture model of the future smart intensive care unit (ICU).

Papers related to the topics of electronic health record (EHR), big data, data flow and clinical decision support in ICUs were investigated. These concepts have been analyzed in combination with secondary use of data, prediction models, data standardization and interoperability challenges. Based on the findings, an architecture model evaluated using MIMIC III is proposed.

Research identified issues regarding implementation of systems, data sources, interoperability, management of big data and free text produced in ICUs and lack of accuracy of prediction models. ICU should be treated as part of a greater system, able to intercommunicate with other entities.

The research examines the current needs of ICUs in interoperability and data management. As environment changes dynamically, continuous assessment and evaluation of the model with other ICU databases is required.

The proposed model improves ICUs interoperability in national health system, ICU staff intercommunication, remote access and decision support. Its modular approach ensures that ICUs can have their own particularities and specialisms while ICU functions provide ongoing expertise and training to upgrade its staff.

Coronavirus disease 2019 (COVID-19) has made telecommuting widely valued, but different individuals have different degrees of acceptance of telecommuting. This article aims to identify suitable individuals for telework and to clarify which types of workers are suitable for what level of telework, set scientific, reasonable hybrid work ratios and processes and measure their suitability.

First, two working scenarios of different risk levels were established, and the theory of planned behavior (TPB) was used to introduce latent variables, constructing a multi-indicator multi-causal model (MIMIC) to identify suitable individuals, and second, constructing an integrated choice and latent variable (ICLV) model of the working method to determine the suitability of different types of people for telework by calculating their selection probabilities.

It is possible to clearly distinguish between two types of suitable individuals for telework or traditional work. Their behavior is significantly influenced by the work environment, which is influenced by variables such as age, income, attitude, perceived behavioral control, work–family balance and personnel exposure level. In low-risk scenarios, the influencing factors of the behavioral model for both types of people are relatively consistent, while in high-risk scenarios, significant differences arise. Furthermore, the suitability of telework for the telework-suitable group is less affected by the pandemic, while the suitability for the non-suitable group is greatly affected.

This study contributes to previous literature by: (1) determining the suitability of different population types for telework by calculating the probability of selection, (2) dividing telework and traditional populations into two categories, identifying the differences in factors that affect telework under different epidemic risks and (3) considering the impact of changes in the work scenario on the suitability of telework for employees and classifying the population based on the suitability of telework in order to avoid the potential negative impact of telework.

This paper aims to study a scale development and validation process for an integrative marketing–operations alignment (MOA) theory. This theory was derived from several distinct theories that have attempted to explain the interaction between marketing and operations functions of manufacturing organizations. An initial qualitative research and literature review identified five antecedents to the MOA construct (decision coordination, reward system, information exchange, leadership strategy and performance evaluation) as well as two outcomes (customer orientation and competitor orientation).

The scale was developed and validated using successive testing processes including exploratory factor analysis, confirmatory factor analysis and structural equation modeling (LISREL).

The outcome of the research is a tested and validated model of MOA. While more work needs to be done to test and potentially extend the theory, this research has produced a basic functional model of the MOA process.

The limitations include target populations, choice of industry and geography and cross-sectional time horizon of the study.

This study represents an original contribution to the organizational theory literature, as it provides both a sound theoretical basis and a validated measurement model for the proposed theory of MOA. While this research does draw on existing models, it is more comprehensive and theory-based than the existing models.

The purpose of this paper is to investigate factors affecting farmers’ perceptions and knowledge of soil testing benefits and fertilizers use in Northern Haiti.

Data were collected from 452 farmers within 17 localities in Northern Haiti. The findings reveal that farmers currently have little or no knowledge of soil testing benefits and but know better about fertilizer use. The soil testing benefits and knowledge on fertilizers use were collected using Likert scale. Analyses were done using structural equations model and choice model.

Factors such as farm size, participation in project, rice, banana and cocoa growers, affect farmers’ perceptions and knowledge of soil testing benefits. Factors affecting willingness to pay include group membership, type of crops grown, whether farmer’ land is on the slope, his farm size and whether he participates in the US Agency for International Development (USAID) project. Knowledge on fertilizer use is influenced by rice and banana growers, fertilizer use, participation in soil testing program and AVANSE/USAID. The effects of both latent variables are found to be positive but non-significant.

As policy implication; farmers need training module to be better informed on soil testing benefits.

Soil testing is a novel agricultural input that is being popularized in developing countries. For sustainability of the laboratory to be installed, this study is needed to fill the gap in research on farmers’ behaviors toward and demand of soil testing in Northern Haiti.