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Journal on Economics, Management and Business Technology
Published by Ihsa Institute
ISSN : -     EISSN : 29620694     DOI : -
Core Subject : Economy, Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering
Journal on Economics, Management and Business Technology, is a Economics, Management and Business Technology published since 2022 by IHSA Institute. Journal on Economics, Management and Business Technology published 2 times a year (March and September), Each issue consists of a minimum of 5 articles, the scope of this journal is Economics, Management and Business Technology.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 42 Documents
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Challenges in Enforcing License Revocation for Companies Violating Employment Laws: Legal, Financial, and Operational Perspectives Fauzi Zalmi; Ghandy Ghandy; Bobby Hidayat
Journal on Economics, Management and Business Technology Vol. 3 No. 2 (2025): March: Economics, Management and Business Technology
Publisher : IHSA Institute

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Abstract

This research examines the challenges faced by regulatory bodies in enforcing license revocation for companies that violate employment laws. Despite the critical role of license revocation in ensuring compliance and protecting workers' rights, regulatory bodies often encounter significant barriers, including legal ambiguities, resource constraints, corporate resistance, political pressures, and complications arising from globalization. The primary objective of this study is to investigate these challenges and assess their impact on the effectiveness of license revocation enforcement. Using a qualitative approach, this research analyzes case studies, interviews with regulatory authorities, and a review of relevant legal frameworks. The findings reveal that unclear legal standards, insufficient resources, and external pressures significantly hinder enforcement efforts, allowing some companies to evade penalties. The study concludes that regulatory bodies need clearer legal frameworks, more resources, and stronger public support to enhance enforcement. This research contributes to a deeper understanding of the obstacles in labor law enforcement and offers insights for future policy improvements and cross-border regulatory collaboration.
An Exploring Consumer Preferences for University Merchandise: The Impact of University Identification, Product Attributes, Social Motivation, Purchasing Experience Raden Rifqi Dwisanto
Journal on Economics, Management and Business Technology Vol. 3 No. 2 (2025): March: Economics, Management and Business Technology
Publisher : IHSA Institute

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Abstract

This study examines the relationship between university identification, product attributes, social and psychological motivation, and purchasing experience with buyer preferences and purchase decisions regarding official Universitas Padjadjaran (Unpad) merchandise. Using the Spearman Rank Correlation method, data were collected from 23 respondents, including students, lecturers, and academic staff at Unpad. The findings indicate a positive and significant correlation between university identification and buyer preference, suggesting that merchandise serves as an identity symbol for students and alumni. Similarly, product attributes such as design, material quality, and exclusivity significantly influence purchasing preferences. Moreover, social and psychological motivation, including peer influence and emotional attachment, was found to be a strong predictor of merchandise preference. Additionally, customer experience in both physical stores and online platforms affected purchasing decisions, emphasizing the need for improved accessibility and service quality. The study highlights that buyer preference directly influences purchase decisions, reinforcing the role of branding, marketing strategies, and consumer engagement in university merchandise sales. The results provide strategic insights for enhancing Unpad’s merchandise marketing efforts, with implications for future research on university branding and consumer behavior in higher education institutions
The Impact of Human-AI Collaboration on Employee Performance in Digital Companies Leutrim Mert
Journal on Economics, Management and Business Technology Vol. 4 No. 1 (2025): September: Economics, Management and Business Technology
Publisher : IHSA Institute

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Abstract

The rapid adoption of Artificial Intelligence (AI) technologies has transformed the operations of digital companies and created new forms of collaboration between employees and AI systems. Human-AI Collaboration has emerged as a strategic approach that combines human creativity, judgment, and adaptability with AI-driven analytics, automation, and decision support capabilities. This study aims to analyze the impact of Human-AI Collaboration on employee performance in digital companies. A quantitative research approach was employed using a cross-sectional survey method involving 286 employees who actively use AI tools in software companies, FinTech firms, e-commerce businesses, and digital marketing agencies. Data were collected through a structured questionnaire using a five-point Likert scale and analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The results indicate that Human-AI Collaboration has a positive and significant effect on employee performance. Specifically, trust in AI, AI usability, and AI reliability were found to be significant determinants of effective collaboration, contributing to higher productivity, improved work quality, greater efficiency, and better decision-making outcomes. These findings suggest that digital organizations should invest in AI literacy, transparent AI governance, and employee-centered AI integration strategies. Future AI implementation should emphasize collaborative intelligence that enhances, rather than replaces, human capabilities.
The Impact of AI-Assisted Recruitment on Employee Recruitment Quality: Evidence from Human Resource Professionals in Digital Organizations Awrad Ghazanfer; Bizhan Bizhan
Journal on Economics, Management and Business Technology Vol. 4 No. 1 (2025): September: Economics, Management and Business Technology
Publisher : IHSA Institute

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Abstract

Artificial Intelligence (AI) has become increasingly integrated into Human Resource Management, particularly in employee recruitment, as organizations seek to improve hiring effectiveness, reduce recruitment time, and enhance the overall quality of recruitment decisions. This study aims to examine the impact of AI-assisted recruitment on employee recruitment quality. A quantitative research approach was employed using a cross-sectional survey of 250 Human Resource (HR) professionals working in digital companies, technology firms, service organizations, and startups that have implemented AI-assisted recruitment systems. Data were collected through a structured questionnaire using a five-point Likert scale and analyzed using Partial Least Squares Structural Equation Modeling (SEM-PLS). The findings reveal that AI-assisted recruitment has a positive and statistically significant effect on employee recruitment quality. Specifically, AI improves candidate-job matching, enhances hiring accuracy, increases recruitment efficiency, strengthens hiring effectiveness, and reduces recruiters' manual workload by automating repetitive administrative tasks. These findings indicate that AI serves as an effective decision-support tool that enables organizations to identify qualified candidates more consistently and efficiently. The study concludes that AI-assisted recruitment can substantially improve employee recruitment quality when implemented responsibly through appropriate human oversight, ethical governance, algorithm transparency, and continuous performance evaluation, thereby supporting more effective and sustainable talent acquisition strategies in the digital era.
The Impact of AI-Based Business Analytics on Corporate Strategic Decision Making Daquan Gosheven; Langundo Philly; Taila Taila
Journal on Economics, Management and Business Technology Vol. 4 No. 1 (2025): September: Economics, Management and Business Technology
Publisher : IHSA Institute

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Abstract

The rapid growth of digital transformation has encouraged organizations to increasingly adopt AI-Based Business Analytics to enhance strategic decision making in highly dynamic and competitive business environments. Artificial Intelligence (AI) technologies, including machine learning, predictive analytics, and real-time data processing, enable organizations to generate faster, more accurate, and evidence-based insights that support executive decision making and improve organizational performance. This study aims to examine the impact of AI-Based Business Analytics on corporate strategic decision making. A quantitative research approach was employed using an explanatory cross-sectional survey design. Data were collected from 300 corporate executives, strategic planning managers, business analysts, senior managers, and AI implementation specialists working in organizations that have adopted AI-enabled business analytics. The data were analyzed using Partial Least Squares Structural Equation Modeling (SEM-PLS) to evaluate the measurement and structural models and test the proposed hypotheses. The findings indicate that AI-Based Business Analytics has a positive and statistically significant influence on corporate strategic decision making. The results also demonstrate that AI-generated insights enable organizations to make more informed and adaptive strategic decisions in increasingly uncertain business environments. The study concludes that adopting AI-Based Business Analytics strengthens organizational strategic decision-making capabilities, enhances sustainable competitive advantage, and supports long-term organizational performance. These findings contribute to the literature on Strategic Management, Artificial Intelligence, Business Analytics, and Decision Science while providing practical guidance for organizations seeking to maximize the strategic value of AI-driven analytics in corporate decision-making processes.
Development of a Smart Business Framework Based on Artificial Intelligence and Sustainability Arya Mawla Kamakshi
Journal on Economics, Management and Business Technology Vol. 4 No. 1 (2025): September: Economics, Management and Business Technology
Publisher : IHSA Institute

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Abstract

The rapid advancement of Artificial Intelligence (AI) and digital technologies has transformed business operations, enabling organizations to enhance efficiency, innovation, and data-driven decision-making. At the same time, increasing stakeholder expectations and global sustainability agendas have highlighted the importance of integrating environmental, social, and governance (ESG) principles into business strategies to achieve long-term competitiveness. However, existing studies often examine AI adoption and sustainability independently, creating a need for a unified Smart Business Framework that combines AI capabilities with sustainability principles. Therefore, this study aims to develop and validate a Smart Business Framework based on Artificial Intelligence and Sustainability. The research employed the Design Science Research (DSR) methodology, supported by a systematic literature review of publications indexed in major scientific databases between 2020 and 2026 and expert validation using a modified Delphi approach. The framework was constructed by synthesizing key concepts from previous studies and subsequently refined through expert evaluation to ensure its theoretical robustness and practical applicability. The results propose a comprehensive framework integrating Artificial Intelligence capability, Digital Transformation, Business Intelligence, Innovation, Governance, Sustainability, Customer Value, and Business Performance into a unified strategic architecture for intelligent and sustainable business transformation. The proposed framework contributes to Smart Business literature by integrating previously fragmented research streams while providing managers and policymakers with practical guidance for implementing responsible AI, promoting sustainable digital transformation, and strengthening organizational competitiveness. Furthermore, the framework establishes a solid foundation for future empirical studies examining AI-driven sustainable business development across various industries and organizational contexts.
The Impact of Carbon Disclosure on Company Competitiveness: Evidence from Publicly Listed Companies Jun Qiaofeng
Journal on Economics, Management and Business Technology Vol. 4 No. 1 (2025): September: Economics, Management and Business Technology
Publisher : IHSA Institute

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Abstract

Carbon disclosure has become an increasingly important aspect of corporate sustainability as companies respond to climate change, growing Environmental, Social, and Governance (ESG) expectations, and expanding regulatory requirements for environmental transparency. Transparent carbon reporting is expected to enhance corporate competitiveness by strengthening stakeholder trust, improving corporate reputation, attracting sustainable investment, and supporting long-term strategic positioning. This study aims to examine the impact of carbon disclosure on company competitiveness. A quantitative explanatory research design was employed using a cross-sectional survey of 180 publicly listed companies that publish sustainability reports. The sample was selected through purposive sampling based on predefined eligibility criteria. Data were collected using structured questionnaires and supported by corporate sustainability reports, and subsequently analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that carbon disclosure has a positive and statistically significant effect on company competitiveness. Companies with higher levels of environmental transparency demonstrate stronger competitive performance through improved corporate reputation, greater investor confidence, enhanced stakeholder trust, and increased operational effectiveness. These findings suggest that effective carbon disclosure serves not only as a regulatory compliance mechanism but also as a strategic business tool for creating sustainable competitive advantage. Therefore, companies are encouraged to integrate carbon disclosure into their broader sustainability initiatives and business strategies to achieve long-term value creation and organizational competitiveness.
The Influence of Artificial Intelligence on the Efficiency of Economic Decision-Making among Generation Z Households in Indonesia Arai Maharati
Journal on Economics, Management and Business Technology Vol. 4 No. 2 (2026): March: Economics, Management and Business Technology
Publisher : IHSA Institute

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Abstract

The rapid advancement of Artificial Intelligence (AI) has transformed financial decision-making by providing intelligent tools that assist individuals in budgeting, expenditure management, savings, investment planning, and financial forecasting. As digital natives, Generation Z has increasingly adopted AI-powered applications such as intelligent financial assistants, budgeting platforms, and generative AI tools to support everyday economic decisions, making it essential to understand how these technologies influence household financial management. Efficient household economic decision-making is critical for achieving financial stability, improving resource allocation, and enhancing long-term financial well-being, particularly in an increasingly digital economy. This study aims to analyze the influence of Artificial Intelligence utilization on the efficiency of economic decision-making among Generation Z households in Indonesia. A quantitative explanatory research design with a cross-sectional survey was employed, involving 374 Generation Z respondents who actively participated in household financial management. Data were collected using a structured questionnaire and analyzed using Structural Equation Modeling with Partial Least Squares (SEM-PLS). The findings reveal that Artificial Intelligence utilization has a significant positive effect on household economic decision-making efficiency, while Technology Acceptance partially mediates this relationship and Financial Literacy strengthens its positive impact. The study concludes that AI can substantially improve budgeting accuracy, spending control, savings management, investment decision quality, and financial planning when supported by adequate financial literacy and responsible technology adoption, thereby contributing to more effective household economic management among Generation Z in Indonesia.
Employee Readiness for Artificial Intelligence Implementation in Companies: An Analysis of the Determinants of Successful AI AdoptionEmployee Readiness for Artificial Intelligence Implementation in Companies: An Analysis of the Determinants of Successful Nasir Ismail; Harun Hassan; Muhammad Harith
Journal on Economics, Management and Business Technology Vol. 4 No. 2 (2026): March: Economics, Management and Business Technology
Publisher : IHSA Institute

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Abstract

The rapid advancement of Artificial Intelligence (AI) has transformed organizational operations and accelerated digital transformation across various business functions, including human resource management, finance, manufacturing, marketing, customer service, logistics, and strategic decision-making. As organizations increasingly invest in AI technologies to improve productivity, operational efficiency, innovation, and competitiveness, employee readiness has emerged as a critical factor determining the success of AI implementation. This study aims to analyze employee readiness toward AI implementation in companies and identify the key factors influencing successful AI adoption. A quantitative research approach employing a cross-sectional survey design was adopted. Data were collected through structured questionnaires using a five-point Likert scale from 328 employees working in organizations that had implemented or were implementing AI technologies. Respondents were selected using purposive sampling, and the data were analyzed using descriptive statistics and Structural Equation Modeling–Partial Least Squares (SEM-PLS). The findings indicate that employees generally demonstrate a moderate to high level of readiness for AI implementation. AI knowledge, digital literacy, organizational support, AI training, leadership support, trust in AI, change readiness, perceived usefulness, and self-efficacy were found to have significant positive effects on employee readiness, whereas technology anxiety negatively influenced employees' willingness to adopt AI technologies. Among these factors, AI training and leadership support emerged as the strongest predictors of employee readiness. These findings contribute to the literature on AI adoption and organizational readiness while providing practical recommendations for organizations to strengthen AI literacy, employee development, leadership engagement, and human-centered AI implementation strategies to ensure sustainable digital transformation.
Dynamic Capability Strategy in Technology-Based Companies: Enhancing Innovation, Organizational Adaptability, and Sustainable Competitive Advantage Mayra Dania; Zara Khadijah; Atiyyatullah Atiyyatullah; Qudrah Qudrah; Sadia Hira
Journal on Economics, Management and Business Technology Vol. 4 No. 2 (2026): March: Economics, Management and Business Technology
Publisher : IHSA Institute

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Rapid technological advancement, digital transformation, artificial intelligence, and increasing global competition have significantly intensified environmental uncertainty for technology-based companies. In such dynamic business environments, organizations must continuously adapt to technological change, market disruption, and evolving customer demands to sustain long-term competitiveness. This study aims to analyze dynamic capability strategies in technology-based companies and evaluate how sensing capability, seizing capability, and transforming capability contribute to organizational competitiveness, innovation performance, organizational performance, and business sustainability. The study employed a quantitative research approach using an explanatory survey design involving managers and executives from technology-based companies operating in software development, artificial intelligence, FinTech, cloud computing, digital platforms, and technology-oriented small and medium-sized enterprises. Data were collected through structured questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that all three dimensions of Dynamic Capability significantly and positively influence organizational outcomes, with transforming capability demonstrating the strongest effect on organizational performance and long-term business sustainability. Sensing capability enhances organizations' ability to identify technological opportunities and market changes, while seizing capability improves strategic resource allocation and innovation implementation. The study concludes that the integration of sensing, seizing, and transforming capabilities enables technology-based companies to effectively respond to environmental changes, accelerate innovation, and maintain sustainable competitive advantage. The findings contribute to the advancement of Dynamic Capability Theory by demonstrating the complementary nature of its core dimensions and provide practical guidance for managers and policymakers in developing adaptive strategies, strengthening organizational learning, promoting digital transformation, and enhancing long-term organizational resilience in the digital economy.

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