cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
,
INDONESIA
Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 25 Documents
 1   2   3 
Search results for , issue "Vol 8, No 5 (2024): October" : 25 Documents clear
Short and Effective: A Reasoned Proposal for Organizational Climate Measurement Ramos, Valentina; Ramos-Galarza, Carlos; Pazmiño, Pablo; Tejera, Eduardo
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-09

Abstract

Generally, organizational climate research does not focus on the work environment because the mindset and emotions of employees are often mistaken for organizational culture. Additionally, surveys to evaluate the organizational climate tend to be long, and therefore, organizational climate studies are conducted only once a year—that too if an organization is concerned about its employees. This research proposes a methodology to evaluate organizational climate; the methodology has the following characteristics: it is a short evaluation named “pulse”; it is oriented toward specific elements of culture that influence the organizational climate and its variability; and it considers organizational contexts. The study was conducted in three organizations encompassing three sectors (N=3,331 employees). The survey included three questions regarding employees’ feelings and climate perception at the individual, group, and organizational levels. Additionally, it had 56 questions related to the elements of organizational culture, grouped into six components after an exploratory analysis: Structure, Recognition, Leadership, Accountability, Work Team, and Ethics. The results showed significant differences between organizations based on the organizational climate perception, its strength, and the behavior of the variables associated with the organizational culture that impacts the climate. Additionally, cultural elements were reduced because of their relationship with the organizational climate. This research suggests that organizational climate studies should be conducted for specific organizational contexts. Additionally, it proposes a methodology to reduce the duration of organizational climate studies by focusing on specific cultural dimensions associated with the climate, which can be applied longitudinally throughout the year to monitor climate changes. Doi: 10.28991/ESJ-2024-08-05-09 Full Text: PDF
Optimizing Injection Molding for Propellers with Soft Computing, Fuzzy Evaluation, and Taguchi Method Hedayati-Dezfooli, M.; Moayyedian, Mehdi; Dinc, Ali; Abdrabboh, Mostafa; Saber, Ahmed; Amer, A. M.
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-025

Abstract

This research explores multi-objective optimization in injection molding with a focus on identifying the optimal configuration for the moldability index in aviation propeller manufacturing. The study employs the Taguchi method and fuzzy analytic hierarchy process (FAHP) combined with the Technique for the Order Performance by Similarity to the Ideal Solution (TOPSIS) to systematically evaluate diverse objectives. The investigation specifically addresses two prevalent defects—shrinkage rate and sink mark—that impact the final quality of injection-molded components. Polypropylene is chosen as the injection material, and critical process parameters encompass melt temperature, mold temperature, filling time, cooling time, and pressure holding time. The Taguchi L25 orthogonal array is selected, considering the number of levels and parameters, and Finite Element Analysis (FEA) is applied to enhance precision in results. To validate both simulation outcomes and the proposed optimization methodology, Artificial Neural Network (ANN) analysis is conducted for the chosen component. The Fuzzy-TOPSIS method, in conjunction with ANN, is employed to ascertain the optimal levels of the selected parameters. The margin of error between the chosen optimization methods is found to be less than one percent, underscoring their suitability for injection molding optimization. The efficacy of the selected optimization method has been corroborated in prior research. Ultimately, employing the fuzzy-TOPSIS optimization method yields a minimum shrinkage value of 16.34% and a sink mark value of 0.0516 mm. Similarly, utilizing the ANN optimization method results in minimum values of 16.42% for shrinkage and 0.0519 mm for the sink mark. Doi: 10.28991/ESJ-2024-08-05-025 Full Text: PDF
Open Government Data Intention-Adoption Behavioural Model for Public Sector Organisations: A Technological Innovation Perspective Khurshid, Muhammad M.; Rashid, Ammar; Yusof, Shafiz; Ahmad, Raja W.; Shehzad, Hafiz Muhammad Faisal
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-04

Abstract

The objective of this research is to examine an open government data (OGD) intention-adoption behavioural model for the public sector organisations (PSOs), since examining the model is expected to lead to a better understanding of how to realise this technological innovation among PSOs on a large scale to excavate its innovative value. In this respect, we proposed a theoretical model to explore the factors that affect OGD adoption behaviour based on three dimensions of the TOE (technology, organisation, and environment) framework. The model was then analysed after collecting the survey data from 249 PSOs in Pakistan using a purposive sampling technique. The findings unfolded that the factors, except centralisation and civil society participation, framed in technology dimension (data resource, dataset quality, perceived benefits), organisation dimension (data-driven culture, digitisation capacity, need for transparency), and environment dimension (compliance pressure, political leadership commitment) affect the PSOs’ OGD adoption intention. Cumulatively, the intention to adopt OGD was found to have a significant positive impact on OGD adoption behaviour. Based on the TOE framework, the model, with the addition of adoption intention as a significant positive factor in adoption behaviour, bears a crucial theoretical and practical contribution in the context of OGD. Doi: 10.28991/ESJ-2024-08-05-04 Full Text: PDF
Zero-Shot Prompting Strategies for Table Question Answering with a Low-Resource Language Jannuzzi, Marcelo; Perezhohin, Yuriy; Peres, Fernando; Castelli, Mauro; Popovič, Aleš
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-020

Abstract

This work explores the application of zero-shot prompting strategies for table question answering (TQA) in Portuguese, focusing specifically on the Text2SQL task. This task involves translating questions posed in natural language into Structured Query Language (SQL) queries, which can be executed against a database to answer the original question. Given the popularity of relational databases across various domains, advancements in this field can substantially impact the accessibility and democratization of data as simpler and more intuitive interfaces for database interaction are developed. Despite this significant potential, progress in developing Portuguese TQA solutions remains limited. The proposed approach leverages Large Language Models (LLMs)—specifically the GPT-3.5 and GPT-4 models—through zero-shot prompting. The primary objectives are to assess the effectiveness of such LLMs in this task and to identify the most suitable prompt styles. These are evaluated using a Portuguese translation of the popular Spider Text2SQL benchmark. Results reveal that the proposed approach can generate adequate SQL queries to answer Portuguese language questions about various databases, mainly when using GPT-4. The findings suggest that including schema information and database content in the prompts is critical for satisfactory outcomes. Doi: 10.28991/ESJ-2024-08-05-020 Full Text: PDF
Demystifying Knowledge Work Practices and Performance in the Public Sector Pachayappan, Neetha Kumari; Aravindan, Kalisri Logeswaran; Alias, Mazni; Ramayah, T.; Annamalah, Sanmugam; Choong, Yap Voon
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-015

Abstract

The performance of the public sector, especially its officers, is vital to a nation’s growth in light of the challenges clouding public service. Despite numerous efforts and initiatives, the level of efficiency of Malaysian public sector officers remains feeble, and public dissatisfaction has led to criticism of the administration. Therefore, addressing issues surrounding the performance of public sector officers is imperative to improve public perception. Guided by Drucker’s knowledge work productivity theory, this research aims to discover the relationship between knowledge work practices toward affective commitment (AC) and knowledge worker performance (KWP). This research adopted a cross-sectional design involving a survey of 395 administrative and diplomatic officers who were recruited via stratified random sampling. A variance-based structural equation modeling using Smart PLS 4.0 was conducted to analyze the data. Results show that job crafting (JC) and continuous learning (CL) improve KWP, job-related innovation (JRI) does not impact KWP, and AC exerts a mediating impact on the relationship between knowledge work practices and KWP. This study provides impetus to knowledge productivity and human behavior by integrating JC into Drucker’s theory. Doi: 10.28991/ESJ-2024-08-05-015 Full Text: PDF

Page 3 of 3 | Total Record : 25

 1   2   3 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 10 No. 1 (2026): February Vol. 9 No. 6 (2025): December Vol. 9 No. 5 (2025): October Vol. 9 No. 4 (2025): August Vol. 9 No. 3 (2025): June Vol 9, No 1 (2025): February Vol. 9 (2025): Special Issue "Emerging Trends, Challenges, and Innovative Practices in Education" Vol 8, No 6 (2024): December Vol 8, No 5 (2024): October Vol. 8 No. 5 (2024): October Vol 8, No 4 (2024): August Vol 8, No 3 (2024): June Vol 8, No 2 (2024): April Vol 8, No 1 (2024): February Vol 8 (2024): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 7 (2023): Special Issue "COVID-19: Emerging Research" Vol 7, No 6 (2023): December Vol 7, No 5 (2023): October Vol 7, No 4 (2023): August Vol 7, No 3 (2023): June Vol 7, No 2 (2023): April Vol 7, No 1 (2023): February Vol 7 (2023): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 6 (2022): Special Issue "COVID-19: Emerging Research" Vol 6, No 6 (2022): December Vol 6, No 5 (2022): October Vol 6, No 4 (2022): August Vol 6, No 3 (2022): June Vol 6, No 2 (2022): April Vol 6, No 1 (2022): February Vol 6 (2022): Special Issue "Current Issues, Trends, and New Ideas in Education" Vol 5 (2021): Special Issue "COVID-19: Emerging Research" Vol 5, No 6 (2021): December Vol 5, No 5 (2021): October Vol 5, No 4 (2021): August Vol 5, No 3 (2021): June Vol 5, No 2 (2021): April Vol 5, No 1 (2021): February Vol 4 (2020): Special Issue "IoT, IoV, and Blockchain" (2020-2021) Vol 4, No 6 (2020): December Vol 4, No 5 (2020): October Vol 4, No 4 (2020): August Vol 4, No 3 (2020): June Vol 4, No 2 (2020): April Vol 4, No 1 (2020): February Vol 3, No 6 (2019): December Vol 3, No 5 (2019): October Vol 3, No 4 (2019): August Vol 3, No 3 (2019): June Vol 3, No 2 (2019): April Vol 3, No 1 (2019): February Vol 2, No 6 (2018): December Vol 2, No 5 (2018): October Vol 2, No 4 (2018): August Vol 2, No 3 (2018): June Vol 2, No 2 (2018): April Vol 2, No 1 (2018): February Vol 1, No 4 (2017): December Vol 1, No 3 (2017): October Vol 1, No 2 (2017): August Vol 1, No 1 (2017): June More Issue