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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.

Background: The study focuses on the challenges encountered during the distribution of food and face-mask items during the first COVID-19 lock-down by various relief supply chain actors.

Methods: Data were collected from forty (40) relief actors through online (via Zoom and telephones) and face-to-face interviews, between January 2021 to March 2021. Data was coded based on per-determined themes after which it was further processed using Atlas ti. v7.57 to generate patterns.

Results: The study established challenges related to needs identification, procurement, warehousing, transportation, handling, beneficiary verification, and last-mile distribution. Additionally, the media and politics coupled with the emergence of new actors and governance issues were part of the challenges identified.

Conclusions: The identified challenges were internal and external to the relief supply chain; hence actors could have control over some while others were beyond their control. The findings could inform practitioners and policymakers on what challenges are likely to affect their operations, especially during a pandemic, and design appropriate coping mechanisms.

The evolving visitors' expectations and the unfolding digital transformation compel rethinking on the service offering of museums and cultural institutions. Although digitalization and people-centeredness are widely exploited to enhance the visiting experience, there is limited evidence of their implications on organizational attractiveness. The article investigates this issue, examining the service attributes that entice visitors.

The study collected secondary data from the latest census study by the Italian Institute of Statistics on museums and cultural institutions. Two hierarchical regression models have been run on a sample of large publicly owned organizations (n = 312) to identify the service factors that were most effective in attracting Italian and foreign visitors.

Museums and cultural institutions undergoing a digital transformation were more effective in attracting visitors. The delivery of virtual tours and online events captivated the Italian audience. Foreigners appreciated the opportunity to use applications augmenting the on-site visit.

Digitalization and people-centeredness improve the attractiveness of museums and cultural institutions. Using digital channels to engage visitors fosters their desire to interact with cultural heritage. Furthermore, digitalization enriches the on-site visit, expanding conventional services with virtuality. However, the adverse effects on cultural heritage should be carefully handled.

This study highlights the service attributes that add to the attractiveness of museums and cultural institutions, enabling them to engage visitors and improve the visiting experience.

The purpose of this paper is to develop and validate a scale for measuring perceived bureaucratic readiness for smart city initiatives.

The present study employs a mixed method approach to achieve its research objectives. An exploratory study, consisting of literature review and qualitative interviews with key informants, was conducted to develop an initial instrument for measuring bureaucratic readiness. An online survey of 40 civil servants involved in smart city programmes in the Yogyakarta City government was then administered to test the instrument’s validity and reliability.

Perceived bureaucratic readiness can be measured through four dimensions: commitment of the upper echelons, legal support, information technology resources and governance.

The proposed scale provides an alternative instrument for measuring perceived bureaucratic readiness for smart city initiatives. However, as data were only derived from one city government, they are relatively small in scope. Future research can be conducted for generalisation by replicating this study in other cities, thereby measuring its effectiveness in other contexts and settings.

This study not only provides a better understanding of bureaucratic readiness for smart city initiatives, but also proposes an assessment tool as a practical means of assessing bureaucratic readiness. The quantification of readiness is beneficial to putting smart city programmes into practice, as it allows smart city managers to assess the internal bureaucracy’s level of readiness. It also allows managers to mitigate and further policy agendas and thereby improve the bureaucracy’s support for smart city programmes.

Literature sometimes underestimates the role of bureaucracy in smart city implementation while overly stressing stakeholders, vendors and technology. This paper attempts to contribute to smart city research by reaching beyond the technological perspective and focusing on local government bureaucracy. None of the extant literature provides a scale for measuring bureaucratic readiness. The study thus proposes a systematic way to develop a means of measuring perceived bureaucratic readiness for smart city programmes.