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The purpose of this study is to examine the gains, challenges and determinants of electronic banking adoption in Nigeria.

This paper applied the generalized structural equation modelling (GSEM) to a large sample of respondents surveyed from five of the six geopolitical zones of Nigeria to model the determinants of electronic banking. In addition to many other advantages, GSEM can be used as a likelihood function. As a result, this paper proposes GSEM as the most appropriate tool for modelling the socioeconomic determinant of electronic banking adoption.

About three-quarter of respondents adopted at least a form of electronic banking. However, only a tenth of users used e-banking for purchase of goods or services, implying low electronic payment adoption. The low adoption of electronic payment was due to poor digital security infrastructure which made users vulnerable to widespread electronic frauds. The findings also show that the adoption of e-banking platforms or services was characterized by users' socioeconomic status. For example, the odds of adopting internet/mobile banking decreases with older users but increase with higher educational attainment and income, whereas the odds of adopting e-banking platforms such as short message service (SMS) and point of sale (POS) banking increases with older users and informally employed users respectively.

For a sustainable cashless economy and financial inclusion in Nigeria, policy consolidation that provides safe e-banking services is necessary. Also, e-banking service providers should deliver specific contents and services that match the physical and economic characteristics of users.

Generalized structural equation modelling (GSEM) is a robust likelihood function method that combines the power of structural equation modelling with the generalized linear model. The application of GSEM to predict the likelihood of adopting a banking technology or Service has not been explored in electronic banking literature. Also, as a fast-growing economy with a heterogeneous population, Nigeria presents an interesting context to study the determinants of electronic banking.

Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study aims to offer a comparative evaluation of GSCA and PLSPM in a predictive modeling framework.

A simulation study compares the predictive performance of GSCA and PLSPM under various simulation conditions and different prediction types of correctly specified and misspecified models.

The results suggest that GSCA with reflective composite indicators (GSCA_R) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCA_R performs slightly better than PLSPM with mode A. For operative prediction, which considers all parameter estimates to generate predictions, both methods perform equally well. GSCA with formative composite indicators and PLSPM with mode B generally lag behind the other methods.

Future research may further assess the methods’ prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones.

When prediction is the primary study aim, researchers should generally revert to GSCA_R, considering its performance for observed and operative prediction together.

This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.

Ghana's economy is largely agrarian, and the business of agriculture is dominated by smallholder farmers who are predominantly rural dwellers. As a result, efforts to lift rural farming households from poverty have been narrowed to the promotion of agricultural development to the neglect of the rural non-farm sector. However, this is fast changing in the advent of a burgeoning rural nonfarm economy and must engage the attention of policy actors. This study thus assesses the effect of non-farm participation on households' level of commercialization of agricultural crops in Ghana.

The study applies a generalized structural equation model (GSEM) to the Ghana Living Standards Survey round 6 dataset, a stratified and nationally representative random sample of 16,772 households in 1,200 enumeration areas.

This study finds that non-farm participation increases the produce sold to output ratio. It is concluded that non-farm engagement by farmers boosts commercialization in Ghana. Thus, for the Ghanaian and similar contexts, agricultural development interventions that incorporate non-farm activities are more likely to be successful in improving livelihoods.

The study uses only the ratio of sales value to output value definition for commercialization and acknowledges use of multiple definitions could be superior.

Various empirical studies have examined the link between the farm and nonfarm sectors. This paper is original in its approach as it tackles an aspect of the subject that has been understudied, namely, an exploration of nonfarm and farm linkages from the perspective of agricultural commercialization.

This study examines chronic illness, disability and social inequality within an exposure-vulnerabilities theoretical framework.

Using the National Survey of Drug Use and Health (NSDUH), a preeminent source of national behavioral health estimates of chronic medical illness, stress and disability, for selected sample years 2005–2014, we construct and analyze two foundational hypotheses underlying the exposure-vulnerabilities model: (1) greater exposure to stressors (i.e., chronic medical illness) among racial/ethnic minority populations yields higher levels of serious psychological distress, which in turn increases the likelihood of medical disability; (2) greater vulnerability among minority populations to stressors such as chronic medical illness exacerbates the impact of these conditions on mental health as well as the impact of mental health on medical disability.

Results of our analyses provided mixed support for the vulnerability (moderator) hypothesis, but not for the exposure (mediation) hypothesis. In the exposure models, while Blacks were more likely than Whites to have a long-term disability, the pathway to disability through chronic illness and serious psychological distress did not emerge. Rather, Whites were more likely than Blacks and Latinx to have a chronic illness and to have experienced severe psychological distress (both of which themselves were related to disability). In the vulnerability models, both Blacks and Latinx with chronic medical illness were more likely than Whites to experience serious psychological distress, although Whites with serious psychological distress were more likely than these groups to have a long-term disability.

Several possibilities for understanding the failure to uncover an exposure dynamic in the model turn on the potential intersectional effects of age and gender, as well as several other covariates that seem to confound the linkages in the model (e.g., issues of stigma, social support, education).

This study (1) extends the racial/ethnic disparities in exposure-vulnerability framework by including factors measuring chronic medical illness and disability which: (2) explicitly test exposure and vulnerability hypotheses in minority populations; (3) develop and test the causal linkages in the hypothesized processes, based on innovations in general structural equation models, and lastly; (4) use national population estimates of these conditions which are rarely, if ever, investigated in this kind of causal framework.

This paper aims to explore the connectivity between social media use, access to migrant networks, information asymmetry and migration intentions.

The study was conducted using data from individuals living in Nigeria and analysed with a generalized structural equation model, which is rare for this kind of research.

The authors find a dual mediating role of the social media and the migrant networks in facilitating migration, i.e. reducing the threshold cost required to migrate and introducing a bias in terms of information asymmetry. While social media and access to migrant networks directly increase migration intentions, this changes when incomplete information is provided. People who use social media and their migrant networks for information are more likely to have information about destination countries than information on the transit risk.

The study adds valuable insights for designing awareness campaigns aimed at reducing irregular migration.

This study contributes to the understanding of the intersection of migration and digitalization

This paper aims to investigate the difference between the impacts of indicators of trade barriers (TBs) on bankrupt enterprises (BEs), new enterprises (NEs) and other enterprises (OEs).

The paper has used a multiple-step approach. At the first stage, the initial data has been collected from interviews with 164 top managers of SMEs in West Azerbaijan in Iran during two periods of 2013–2015 and 2017–2019. At the second step, multiple correspondence analysis has been used to summarize the relationships between variables and construct indices for different groups of TBs. Finally, the generalized structural equation model method was used to examine the impact of export barriers.

The results showed that the political legal index is the main TBs for BEs and NEs, but it had a more significant impact on BEs; the financial index was the second major TBs factor for BEs, while OEs did not have a problem in performance index, and the financial index was classified as a minor obstacle for them. All indicators of marketing barriers (except production index) had a negative and significant effect on all enterprises; the most important TBs for NEs was the information index.

The results indicated that if enterprises have a strong financial system and function, they can lessen the impact of sanctions and keep themselves in the market.

This study aims to analyze the factors affecting tea-producing farmers' intention to use e-commerce exchanges (ECEs) to sell their products, combining the technology acceptance model (TAM) theory and barrier factors.

The authors use the generalized structural equation modeling (GSEM) to analyze the intermediate model that is built on TAM.

Research results show that perceived usefulness (PU) and perceived ease of use (PEU) significantly influence farmers' intention to use ECEs to sell their products. However, knowledge and information barriers hinder farmers' intention to use such ECEs.

Encouraging farmers to use ECEs is the most helpful solution for agricultural economic development in the context of the Covid-19 pandemic.

From an academic perspective, this is the first study that combines the TAM theory of Davis (1989) and barrier factors to analyze farmers' intention to use ECEs. The findings are valuable references for policymakers to propose strategies for agricultural economic development during the current pandemic. At the same time, the empirical results obtained from this study provide good orientations for agricultural economic development 4.0 in the future.

This study aims to empirically examine the relationship between the leader characteristic of humility and the informal learning of team members. It also evaluates the role of leader authenticity in mediating that relationship.

Data were collected on 518 salespeople reporting to 66 managers in a time-lagged study of a financial services firm. Generalized structural equation modeling was used to analyze the data and test a multi-hierarchical mediation model.

Leader humility has a significant positive direct and indirect effect on individual informal learning in team contexts, and leader authenticity partially mediates this relationship.

This study advances research on how leaders can help transform learning from a risky endeavor to a daily practice. It shows the impact of the leadership characteristic of humility and explains how humble behaviors provide a model for individual learning in team contexts. It also reveals that leader authenticity is a key mechanism through which leader humility comes to influence employees’ informal learning within work teams.

This study provides empirical support for the importance of leader humility in engendering the trust required for employees to engage in everyday workplace learning. It integrates social information processing theory with social learning theory to show that humble leaders provide critical information about the value, cost and methods of individual informal learning in team contexts. Leader humility increases employees’ beliefs that they can and do learn from working in teams because employees perceive the humble leader’s behaviors as representing the leader’s true intentions.

During the coronavirus disease 2019 (COVID-19) pandemic, public health risk communication has been a vital work. Students account for a large proportion of the population and are often highly mobile; thus, they face a high risk of contagion and spreading the disease. Therefore, risk communication to this group during the pandemic has been essential. This research examines the relationship between risk communication to students and their appropriate behaviors (compliance with COVID-19 preventive measures and COVID-19 information sharing).

This research used structural equation model (SEM) and generalized structural equation model (GSEM) to analyze the data collected from students during the COVID-19 pandemic.

The findings revealed that risk communication in mass media was positively associated with students' compliance with preventive measures and sharing pandemic-related information. Additionally, their compliance behavior was positively associated with their information-sharing behavior.

This research offers some implications regarding the containment of a highly-infectious virus, especially for the context when the risk of outbreak is high and an effective vaccine is not available, by focusing on risk communication and compliance and information-sharing behaviors.

This research is one of the early attempts to examine the risk communication to students, their compliance with preventive measures and their information-sharing behavior during a pandemic.

This study analyzes the factors that affect the decisions of micro-businesses to switch to enterprises in Thanh Hoa, Vietnam.

In order to explain the behavior of micro-businesses in switching to enterprises, the authors designed a questionnaire and collected survey data from 404 micro-businesses. The generalized structural equation modeling was applied to analyze the structured model with the dichotomous dependent variable.

First, the behavior of micro-businesses to switch was indirectly affected by needs and directly affected by attitudes, perceived behavioral control, subjective norm and institutional quality. Second, the subjective norms exerted the most influence on the behavior of the micro-businesses in switching. This finding implies that (1) the behavior of the micro-businesses depended on the assessments, perceptions and views of their friends, relatives and colleagues, and (2) the behavior to switch stems mainly from the desire to improve the businesses' position in society. Finally, both formal and informal institutions play an essential role in the behavior of the micro-businesses to switch to enterprises.

There have been no previous studies that have applied structured models to test the behavior of micro-businesses to switch to enterprises. Furthermore, previous studies had mainly addressed the psychological factors or needs of micro-businesses. In this study, apart from the above factors, the authors discuss the impact of institutions on the behavior of micro-businesses to switch.