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Aptisi Transactions on Technopreneurship (ATT)
Published by Asosiasi Perguruan Tinggi Swasta Indonesia
ISSN : 26558807     EISSN : 26568888     DOI : https://doi.org/10.34306
Core Subject : Economy, Humanities, Science, Education, Social,
Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences
APTISI Transactions on Technopreneurship (ATT) is an international triannual open access scientific journal published by  Pandawan Sejahtera Indonesia. ATT publishes original scientific researchers from scholars and experts around the world with novelty based on the theoretical, experimental, and numerical framework. The Journal aims to make noteworthy contributions to knowledge across the globe through the publication of original, high-quality research articles in the area of entrepreneurship and the impact of emerging technologies on it. In addition to original research articles, APTISI Transactions on Technopreneurship (ATT) publishes reviews, mini-reviews, case reports, letters to the editor, and commentaries, thereby providing a platform for reports and discussions on cutting edge perspectives in the domain of entrepreneurship. All submitted papers will undergo the strict single-blind peer-reviewing process. The Journal is dedicated to publishing manuscripts via a rapid, impartial, and rigorous review process. Once accepted, manuscripts are approved free online open-access instantly upon publication, allowing users to read, download, copy, distribute, print, search, or link to the full texts, thus providing access to a broad readership.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Sistem Informasi Menejemen
Articles 25 Documents
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Search results for , issue "Vol 6 No 3 (2024): November" : 25 Documents clear
Towards Entrepreneurial Campus Sustainability: Integrating Artificial Intelligence for Resource Allocation in Business Management Juanda, Juanda; Riansyah, Reza Juang; Arsadi, Arsadi; Bethany, Laurens
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 3 (2024): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i3.410

Abstract

This research delves into the utilization of artificial intelligence (AI) within the framework of campus resource allocation, with a primary focus on enhancing business management practices and fostering entrepreneurial sustainability within educational institutions. Through an innovative amalgamation of AI technology and SmartPLS methodology, the study constructs a comprehensive analytical framework aimed at tackling the multifaceted challenges inherent in resource allocation within campus environments. The findings underscore the transformative potential of AI integration in optimizing resource utilization, identifying efficiency gains, and nurturing entrepreneurial endeavors. This paper distinguishes itself from existing studies by presenting a novel approach that emphasizes the unique contributions of AI-driven solutions in both methodological innovation and practical application. By harnessing SmartPLS alongside AI, the research facilitates more accurate resource demand forecasting and enables adaptive decision-making processes, thereby contributing to the Sustainable Development Goals (SDGs), particularly in promoting quality education and sustainable management practices. The study also provides a detailed technical implementation of AI algorithms, offering valuable insights into their development and application within campus settings. The broader implications for the educational sector are explored, considering the scalability and adaptability of the proposed solutions in various educational contexts. Furthermore, the research contributes to theoretical advancements by pioneering the integration of AI and SmartPLS in campus management research, offering a fresh perspective on economic, environmental, and social impact assessments of AI-driven solutions.
Cyberpreneurship Research Trends and Insights from 1999 to 2023 Aprillia, Ariesya; Kuswoyo, Chandra; Kristiawan, Allen; Sunarjo, Richard Andre; Awhina, Ridan Ahsani Te
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 3 (2024): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i3.421

Abstract

Cyberpreneurship, as a subset of entrepreneurship, is an emerging field focusing on the use of digital platforms and technologies in business. This study aims to analyze the development and trends in cyberpreneurship research over the past 17 years (1999-2023). The objective s to map the landscape of cyberpreneurship publications, identifying key countries, institutions, journals, authors, and keywords that dominate the field. Using the Dimensions citation indexing database, a bibliometric analysis was conducted, employing VOSviewer for visualizing the relationships among authors, institutions, and keywords. Data was examined based on publication pairs, co-authorship networks, and keyword co-occurrence. The analysis shows that Malaysia leads in cyberpreneurship research, with Multimedia University as the most influential institution, and Education and Training as the most cited journal. The research also highlights a significant growth in cyberpreneurship studies, especially in the last few years, indicating its increasing importance post-COVID-19. The findings suggest that cyberpreneurship is still in its early stages, with substantial potential for future research, particularly in the application of artificial intelligence and digital business models. Further exploration is recommended, especially in countries like Indonesia, where digital entrepreneurship is rapidly evolving.
The Influence of Leadership Dynamics and Workplace Stress on Employee Performance in the Entrepreneurial Sector and the Moderating Role of Organizational Support Ahli, Reem; Hilmi, Mohd Faiz; Abudaqa, Anas
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 3 (2024): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i3.424

Abstract

Employee performance is crucial to the success of organizations, particularly in the entrepreneurial sector. Various factors, such as abusive supervision, job stress, turnover intention, and agile leadership, play a significant role in determining performance outcomes. This study aims to evaluate how perceived organizational support moderates the effects of these factors on employee performance in public firms in the UAE. Data were gathered from a valid sample of 211 respondents. The analysis employed measurement and structural model assessments using Smart PLS version 4.0. The results validated the internal consistency reliability, convergent validity, and discriminant validity of the latent constructs. Structural equation modeling indicated significant effects of agile leadership, abusive supervision, and job stress on employee performance. Furthermore, perceived organizational support was found to significantly moderate the relationships between agile leadership and employee performance, abusive supervision and employee performance, and job stress and employee performance. Based on these findings, several policy recommendations were made for public sector firms in the UAE, particularly highlighting the importance of supportive leadership practices in enhancing employee performance within the entrepreneurial sector.
Artificial Intelligence Model for Detecting Tax Evasion Involving Complex Network Schemes Nuryani, Nuryani; Mutiara, Achmad Benny; Wiryana, I Made; Purnamasari, Detty; Putra, Souza Nurafrianto Windiartono
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 3 (2024): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i3.436

Abstract

Tax evasion through complex network schemes poses a significant challenge to tax authorities, leading to substantial revenue losses. This paper aims to develop and evaluate an artificial intelligence model designed to detect tax evasion within complex corporate networks, providing a comprehensive overview and prediction of tax avoidance behaviors. Employing a systematic literature review and document analysis of applicable tax regulations, the study utilizes Social Network Analysis (SNA) as a primary technique for mapping and analyzing taxpayer networks. The process involves matching taxable identities, constructing taxpayer graphs, extracting features, and developing a machine learning model. The proposed architectures and processes demonstrate the potential for tax authorities to enhance their capabilities in detecting tax evasion involving complex networks, with the machine learning model effectively identifying features related to both individual and network characteristics of taxpayers. The findings suggest that the integration of artificial intelligence and big data analytics can significantly improve the detection of tax evasion in complex corporate structures, offering valuable tools for tax authorities to better enforce tax compliance.
Blending Work Values, Engagement, and Satisfaction to Drive OCB in Technopreneurial Startups Meria, Lista; Hidayat, Syamsul; D. Santiago, Nila; Saukani, Saukani; Husnul Khotimah, Sita
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 3 (2024): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i3.449

Abstract

In startup companies, financial and other resources are often still limited, so human resources are a valuable asset that plays a crucial role in running an entrepreneur. Organizational Citizenship Behaviors (OCB) support new companies or startups success and growth. OCB can increase efficiency and productivity, create a positive work environment, and improve the ability to adapt to change. Therefore, startup companies must encourage and foster OCB among employees to achieve sustainable competitive advantage. This research analyzes the factors influencing OCB by exploring the role of work values, engagement, and job satisfaction. The participants in this research were 351 employees of startup companies in Jakarta. The sampling technique uses simple random sampling, and data analysis uses SEM-PLS. The research findings state that work values positively impact engagement and job satisfaction. Then, work engagement and job satisfaction were ultimately proven to increase employee OCB in startup companies. This study enriches the literature, especially in managing human resources in organizations, and becomes a basis for companies to create rules and policies to increase OCB in a dynamic and competitive business.
Understanding Technopreneurship in Agricultural E-Marketplaces Lestari, Etty Puji; Prajanti, Sucihatiningsih Dian Wisika; Adzim, Fauzul; Primayesa, Elvina; Ismail, Muhammad Iqbal Al-Banna; Lase, Sepandil Laras
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 3 (2024): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i3.454

Abstract

Competition in the global market is challenging for technopreneurs to develop strategies that provide a comparative advantage to win the competition. The article aims to develop a model for applying agricultural product e-marketplaces, including the involvement of related stakeholders in Semarang and Magelang Regency, Indonesia. The study employs two primary analytical methods: the MACTOR framework, which assesses alliances, conflicts, and strategic recommendations, and the Analytical Hierarchy Process (AHP) to prioritize decision-making criteria. The results showed that developing agricultural product e-marketplaces requires collaboration from various stakeholders. Notably, consumers, who play a crucial role in the success of the e-marketplace, emerge as the most influential actors, while middlemen are identified as the most dependent. The primary challenge in developing an agricultural product e-marketplace is ensuring smooth food distribution. At the same time, alternative priorities include increasing business partnerships between local agricultural cooperatives and entrepreneurs/investors and providing infrastructure to support the development of e-marketplaces. This study emphasizes the importance of collaboration between various stakeholders in e-marketplace development and implementation of agricultural products so that they can be aligned for the success of the entire e-marketplace system.
Understanding Data-Driven Analytic Decision Making on Air Quality Monitoring an Empirical Study Sembiring, Irwan; Manongga, Danny; Rahardja, Untung; Aini, Qurotul
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 3 (2024): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i3.459

Abstract

Air quality monitoring is increasingly relying on data-driven analytic decision-making tools to provide accurate and timely information, forming the background of this study. The objective is to understand the factors influencing the adoption and usage behavior of these tools using the Unified Theory of Acceptance and Use of Technology (UTAUT2) model. The method involves incorporating UTAUT2 constructs Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Price Value (PV), Hedonic Motivation (HM), and Habit (H), alongside external variables such as Considered Risk (CR) and Considered Trust (CT). Data from 287 respondents were analyzed to assess their impact on Behavior Intention (BI) and Usage Behavior (UB). The results demonstrate that both trust and risk considerations significantly affect user behavior, underscoring the need to address these factors to enhance the adoption of air quality monitoring systems. In conclusion, this research provides valuable insights for developers and policymakers on improving the implementation and acceptance of data-driven technologies in environmental monitoring, thereby contributing to more effective air quality management.
Navigating E-Commerce Loyalty: The Role of E-Brand Experience and Mediating Factors in Indonesian Millennial Consumers Setiawan, Sandy; Susan, Marcellia; Istiharini, Istiharini
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 3 (2024): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i3.460

Abstract

The rapid growth of e-commerce has driven advancements in internet technology, attracting consumer attention due to its benefits such as flexibility, broader market reach, lower operational costs, faster transactions, a wider range of products, and overall convenience. E-loyalty is crucial for online businesses because customer retention is directly linked to increased profits, making loyal customers the most valuable asset for any company. Millennials, who are wealthy, educated, and tech-savvy, represent the main group engaging in online shopping. To compete in a crowded market and build lasting customer relationships, marketers are increasingly focusing on enhancing the e-brand experience. This study examines the impact of e-brand experience on e-loyalty, with e-commerce in Indonesia serving as a mediating factor among millennials. An online survey distributed via social media collected 516 responses, with 438 meeting the criteria of having made at least two purchases within the past three months. The findings reveal that e-brand experience significantly influences e-trust, satisfaction, and loyalty among millennial consumers. Millennials trust in an e-commerce brand is strongly shaped by their experiences, which are further reinforced by positive feedback from others. A positive brand experience not only increases brand satisfaction but also fosters a strong desire for repeat purchases. For e-commerce platforms, providing excellent customer service and simple, user-friendly processes are essential for building and maintaining customer trust and loyalty.
Leveraging Machine Learning Models to Enhance Startup Collaboration and Drive Technopreneurship Wijono, Sutarto; Rahardja, Untung; Purnomo, Hindriyanto Dwi; Lutfiani, Ninda; Yusuf, Natasya Aprila
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 3 (2024): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i3.462

Abstract

In the dynamic and competitive realm of startups, identifying and cultivating effective collaborations is crucial for sustained success. This research evaluates how machine learning (ML) technologies can enhance startup collaborations by advancing decision-making processes through the analysis of historical data. Employing the SmartPLS methodology, this study collected data from 220 stakeholders, including 207 actively engaged in startups that are either utilizing or integrating ML technologies. The investigation focuses on understanding ML models, the importance of historical data, and the dimensions of collaboration critical to the success of startups. Through analysis with PLS-SEM, it was found that ML models significantly boost inter-startup synergy and the effectiveness of collaborative efforts. The results provide vital insights for industry practitioners and strategic decision-makers, offering practical strategies to employ ML in optimizing collaboration and ensuring sustainable growth within the technopreneurship arena. This study not only highlights the benefits of ML in fostering cooperative ventures but also aims to refine the strategic frameworks essential to the startup ecosystem.
Technopreneurship in Healthcare: Evaluating User Satisfaction and Trust in AI-Driven Safe Entry Stations Rahardja, Untung; Sunarya, Po Abas; Aini, Qurotul; Millah, Shofiyul; Maulana, Sabda
Aptisi Transactions On Technopreneurship (ATT) Vol 6 No 3 (2024): November
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v6i3.489

Abstract

The development of technology in the health sector has encouraged the adoption of technopreneurship, especially in the application of artificial intelligence (AI) to support the safety and efficiency of health services. One of the innovations that has emerged is the AI-driven Safe Entry Station, which is designed to improve the safety and comfort of patients and medical personnel. However, the success of implementing this technology is highly dependent on the level of user satisfaction and trust. This study aims to evaluate the level of user satisfaction and trust in Safe Entry Stations in the health care environment and also explore the variables that influence the acceptance of this technology among users. This research method uses a quantitative approach with a survey involving 673 respondents from various health institutions that have used Safe Entry Stations. Data were analyzed using Structural Equation Modeling (SEM) with SmartPLS 4.0 software to identify the relationship between User Satisfaction (US), trust (TR), behaviour intention (BI), usage behaviour (SB) and technopreneurial impac (TI). The results showed that US and TR significantly influences BI and UB. Additionally, BI strongly impacts TI, suggesting that stronger intentions lead to a greater perceived impact on technopreneurship. This study found that AI-driven Safe Entry Stations has great potential for widespread adoption in the healthcare sector. These findings provide important insights for further development of this technology as well as technopreneurship strategies in the healthcare sector.

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