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This study investigates the determinants of digital census for population and housing census (PHC) program through the lens of performance expectancy, technology readiness, self-efficacy and hedonic motivation for the upliftment of a national data collection exercise and development of human resource management.

A quantitative and qualitative research method was used to survey enumerators' responses from the PHC exercise during the COVID-19 period in Ghana. Based on the four determinants, a conceptual framework was developed consisting of eight proposed hypotheses tested through a structural equation model.

The findings of the study indicate that technological readiness, self-efficacy and hedonic motivation significantly influence behavioural intention to adopt digital technologies for PHC training and data collection. Importantly, the authors identified four key themes relating to digital technologies in PHC – personal enablers, general enablers, inherent affordances (inherent possibilities by the user in relation to what the technology offers in context) and personal inhibitors.

For research, this work systematizes antecedents from diverse research streams and validates their relative impact on government digital transformation for accurate data, thus providing a cohesive theoretical explanation of digital technologies in PHC. Due to the study's infancy in a developing country context, the findings provide a preliminary foundation and constructive insight for a digitalization plan conducive to people’s personality and technological readiness.

Based on the job demands–resources (JD-R) model, this study aims to answer a key research question, i.e. can the job characteristics (i.e. job demands and resources) affect intention to retire early? Additionally, a mediating effect of emotional exhaustion and organizational commitment on the relationships of job demands and job resources, respectively, with early retirement intentions has been explored in the study.

The data has been collected from survey of 450 employees from the banking sector in the state of Punjab (India). A structured questionnaire adapted from past literature has been used as survey instrument for the study. Partial least squares structural equation modelling has been applied in the study using latest version of SmartPLS (version 3.2.8) software.

Both job resources and job demands have a direct significant impact on early retirement intentions. Moreover, a significant partial mediation effect of emotional exhaustion and affective organizational commitment has also been found out on the relationship of job demands and job resources with early retirement intentions, respectively.

The study makes incremental contribution by highlighting the role of both deterrent and motivational factors that either instigate or discourage early retirement intentions among employees. It offers valuable insights for the organizations to use efforts for curtailing the excessive job demands that lead to emotional exhaustion and further result in early retirement intentions. Besides this, adequate job resources should be provided to the employees that lead to the development of affective organizational commitment, which further helps in sustaining the workforce until their actual retirement age.

The paper aims to explore the effect of problem-based learning (PBL) embedding degree and education level on individual perception, as well as the moderating effect of nationality.

The paper first conceptualizes PBL embedding degree which means the extent of applying PBL. It takes an empirical study on an international MBA class in one of the first-class universities in China. An investigation is taken with the designed “PBL-based Cognitive Perception Scale” and an Ordered Probit Model is constructed.

The findings of this study are as follows: PBL embedding degree has a significant effect on the cognitive perception of student, which varies in different dimensions; the educational level of international student positively affects the cognitive perception toward PBL; and nationality may moderate the relationship between the PBL embedding degree and individual perception.

The paper replenishes the investigation and application of Bloom’s Taxonomy of Learning. By conceptualizing PBL embedding degree, the paper extends the research perspectives of PBL and proposes a subjective method on the evaluation of PBL. The paper also may provide a guidance for PBL curriculum design with sustainable development of education.

The authors aim to study a conceptual model based on behavioural theories (UTAUT-3 model) to evaluate the adoption, usage and recommendation for neobanking services in India.

The authors propose this model based on the UTAUT-3 integrated with perceived risk constructs. Hypotheses were developed to determine the relationships and empirically validated using the PLSs-SEM method. Using the survey method, 680 Delhi NCR respondents participated in the survey.

Empirical results suggested that behavioural intention (BI) to usage, adoption and recommendation affects neobanking adoption positively. The research observed that performance expectancy (PE), effort expectancy (EE), perceived privacy risk (PYR) and perceived performance risk (PPR) are the essential constructs influencing the adoption of neobanking services.

Limited by geographic and Covid-19 constraints, a cross-sectional study was conducted. It highlights the BI of neobanking users tested using the UTAUT-3 model during the Covid-19 period.

The study's outcome offers valuable insights into Indian Neobanking services that researchers have not studied earlier. These insights will help bank managers, risk professionals, IT Developers, regulators, financial intermediaries and Fintech companies planning to invest or develop similar neobanking services. Additionally, this research provides significant insight into how perceived risk determinants may impact adoption independently for the neobanking service.

The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy.

The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model.

The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2.

This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics.