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The Eastasouth Management and Business
Published by Eastasouth Institute
ISSN : 29857120     EISSN : 29633591     DOI : https://doi.org/10.58812/esmb
Core Subject : Science,
Decision Sciences, Operations Research & Management
ESMB - The Eastasouth Management and Business is a peer-reviewed journal and open access three times a year (March, July and November) published by Eastasouth Institute. ESMB aims to publish articles in the field of Strategic management, Operations management, Marketing and Sales, Supply chain and logistics, Human resource management, Leadership, and organization management, International business, Sustainable business, Information technology management, Risk and security management, Business ethics and corporate social responsibility. ESMB accepts manuscripts of both quantitative and qualitative research. ESMB publishes papers: 1) review papers, 2) basic research papers, and 3) case study papers.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 199 Documents
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A Comprehensive Financial and Organizational Analysis of Telerehabilitation Business Models for Sustainable Growth and Market Expansion in the Healthcare Sector Dhirenbhai Kalal
The Eastasouth Management and Business Vol. 2 No. 03 (2024): The Eastasouth Management and Business (ESMB)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esmb.v2i03.1143

Abstract

This shift marked the end of the pandemic era, where telerehabilitation was merely an emergency measure, and the beginning of a new growth trajectory for digital health, with the global market estimated at USD 5.32 billion in 2024 and projected to grow to USD 11.81 billion by 2030 at a CAGR of 13.2 per cent. However, the financial and organisational basis of this growth is poorly documented, to varying degrees, in the different clinical indications. This paper presents a financial and organisational evaluation of fourteen peer-reviewed and industry-based sources to create a holistic view of business models for telerehabilitation. Evidence is presented under three themes: Business model frameworks in digital health, Health-economic evidence for telerehabilitation, and Clinical and organisational implementation. Results found that per-patient savings ranged from USD 565.66 to USD 2,352.00, with four of eight studies showing moderate results for combined neurological and cardiological populations; per-patient savings were found to be favourable, with ninety-two per cent of reviewed cardiac studies showing favourable results for protocolized, exercise-based cardiac interventions; and results were least consistent for heterogeneous musculoskeletal indications. Financial sustainability of telerehabilitation businesses was identified through business model syntheses as having common elements such as diversified revenue streams, partnerships with payers and with technology vendors, and adaptive governance. Analysis shows that rather than a common telerehabilitation approach, business model design is the most defendable path to sustainable market expansion.
A Strategic Operations Management Framework for Enhancing Service Efficiency, Therapist Productivity, and Patient Flow Optimization in Outpatient Physical Therapy Clinics Dhirenbhai Kalal
The Eastasouth Management and Business Vol. 2 No. 02 (2024): The Eastasouth Management and Business (ESMB)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esmb.v2i02.1144

Abstract

Outpatient Physical Therapy (OPT) clinics are facing increasing pressure to balance patient demand, strict therapist staffing levels, patient non-attendance and productivity demands dictated by reimbursement requirements. This paper brings together results from twenty peer-reviewed and thesis-level papers covering healthcare scheduling optimization, physical therapist productivity measurement, patient no-show prediction, and lean process redesign to present an integrated Strategic Operations Management Framework for outpatient physical therapy environments. It combines a predictive scheduling optimization system with a real-time capacity allocation system for therapists and a lean-based patient-flow monitoring loop, based on the evidence gained from a discrete-event simulation and mathematical programming. Synthesized results show that a simulation-optimization scheduling approach can increase therapist utilisation to around 91 per cent and decrease the incidence of no shows up to 27 per cent over rule-based block scheduling, and lean healthcare interventions can lead to a median decrease in patient waiting time by 31 per cent and process cycle time by 26 per cent. Productivity indices that have been created over the past 40 years from departmental evaluations reveal a cumulative increase of about 58 percent, while documented risks of clinician burnout have occurred when trying to increase productivity without workforce protections. This proposed framework is divided into four interacting zones: predictive scheduling, lean process redesign, dynamic capacity management and workforce well-being, and provides a staged implementation roadmap. Limitations associated with the retrospective and cross setting nature of the synthesized evidence are discussed, as are implications for clinic administrators, payers, and workforce policy.
Semantic AI-Orchestrated Cross-Domain Automation Framework for Sustainable Smart Factories Milan Bharatkumar Makwana
The Eastasouth Management and Business Vol. 3 No. 02 (2025): The Eastasouth Management and Business (ESMB)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esmb.v3i02.1145

Abstract

Manufacturing sectors pursuing Industry 5.0 objectives increasingly require automation architectures capable of reasoning across heterogeneous cyber-physical domains while advancing sustainability targets. This paper synthesizes evidence from eighteen references spanning semantic web technologies, cyber-physical systems, digital twins, blockchain-enabled traceability, and multi-agent reinforcement learning to propose a Semantic AI-Orchestrated Cross-Domain Automation Framework for sustainable smart factories. The framework integrates a five-layer architecture, comprising physical sensing, cyber-physical integration, semantic reasoning, cross-domain orchestration, and sustainability decision layers, into a unified reasoning pipeline in which ontology-driven knowledge graphs mediate interoperability among heterogeneous equipment, enterprise systems, and human operators. Comparative synthesis indicates that semantic-based autonomous computing architectures reduce unplanned downtime by as much as 37 percent, while multi-agent reinforcement learning scheduling raises resource utilization to 88 percent relative to 58 percent under conventional rule-based scheduling. Interoperability standards such as OPC-UA demonstrate adoption rates near 78 percent among reviewed implementations, and hybrid semantic-blockchain ledgers achieve transaction throughput exceeding 2,100 transactions per second at latencies below 40 seconds, outperforming public proof-of-work ledgers by more than two orders of magnitude. Digital twin adoption trajectories synthesized from the references rose from approximately 8 percent in 2016 to 66 percent by 2024, correlating with a 24 percent reduction in energy consumption and a 31 percent reduction in material waste across reviewed sustainable manufacturing cases. The findings imply that semantic orchestration, combined with decentralized ledgers and human-centric digital twins, offers a scalable pathway toward resilient, low-carbon, and economically viable smart factory operations, while highlighting persistent challenges in ontology standardization, explainability, and cross-organizational governance.
Causality-Preserving Distributed Decision Fabrics for AI-Native Financial Enterprise Architectures Nithesh Gudipuri
The Eastasouth Management and Business Vol. 1 No. 03 (2023): The Eastasouth Management and Business (ESMB)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esmb.v1i03.1169

Abstract

Today, financial enterprise architectures are increasingly incorporating artificial intelligence (AI) capabilities into distributed transaction-processing systems, creating a tension between the need for rapid service availability and the need for a causally coherent audit trail of AI-made decisions. In this paper, we marshal research on causal consistency models, causal inference, explainable AI (XAI), and blockchain-based financial infrastructure to propose a conceptual framework called the causality-preserving distributed decision fabric. Causal consistency, which is formalized in causal-memory and session-guarantee models, is explored as a compromise between the high availability of eventual consistency and the coordination cost of linearizability, which is limited by the CAP theorem. Methods for causal inference and model-agnostic explainability, such as Shapley-value attribution and local surrogate modeling, are examined as ways to make automated financial decisions interpretable to those who manage the risks in financial institutions and regulators. Distributed ledger architectures are viewed as an audit trail that can track and order AI-backed decisions between organizational lines. The synthesis suggests that none of the single mechanisms alone meet the simultaneous requirements of availability, explainability, and auditability; thus, a layered architecture of causal-consistency protocols, an explainability layer, and a permissioned ledger is proposed. Comparisons on a conceptual level regarding consistency models and explainability methods, as well as architectural diagrams depicting the proposed layering, are presented. The findings suggest that maintaining causality is a key prerequisite for trustworthy AI-native financial systems, but that interoperability and computational load are major obstacles to implementation.
The Role of Fintech in Moderating the Impact of Green Finance on Green Innovation in Manufacturing Companies in Indonesia Rifatin Cholidia; Suripto Suripto; Kussuyatmono Bagus Wardianto; Maulana Agung Pratama
The Eastasouth Management and Business Vol. 5 No. 01 (2026): The Eastasouth Management and Business (ESMB)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esmb.v5i01.1002

Abstract

This study aims to analyze the effect of green finance on green innovation with fintech acting as a moderating variable in manufacturing companies in Indonesia. A quantitative approach was used with a causal design and panel data on 12 companies during the 2017–2024 period (96 observations). The analysis was conducted using the Random Effect Model and Moderated Regression Analysis (MRA). The results indicate that green finance does not have a significant effect on green innovation, as proxied by ISO 14001 certification. Meanwhile, fintech was found to have a positive and significant effect on green innovation; however, fintech does not act as a moderating variable in the relationship between green finance and green innovation. These findings confirm that fintech acts as a direct determinant, not as an enhancer of the green finance relationship.
The Mediating Role of Employee Engagement in Explaining the Phenomenon of Performance Stagnation: A Study on the Effectiveness of Training and Development in the Forestry Industry (PT SBA Wood Industries) Muhammad Ilham; Fariz Fariz
The Eastasouth Management and Business Vol. 5 No. 01 (2026): The Eastasouth Management and Business (ESMB)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esmb.v5i01.1107

Abstract

Although PT Sebangun Bumi Andalas Wood Industries (PT SBA WI) invested heavily in training, reaching 86.3 percent of its workforce in 2023, the firm continued to experience flat employee performance. For two successive years, most staff stayed within the average performance band, while the level of work engagement had not yet met the target set by management. Such a pattern points to a mediating mechanism that remains empirically unclarified in the forestry sector. The present research therefore investigates how the effectiveness of training and development shapes work engagement and performance, and whether engagement carries the influence of training onto performance at PT SBA WI. Drawing on Social Exchange Theory (SET) together with Job Demands Resources (JD-R) Theory, the study adopts a quantitative and causal design. Responses were gathered from 224 staff members selected through proportionate stratified random sampling using a questionnaire scored on a Likert scale, and the data were processed with Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4.0. The estimates reveal that training effectiveness raises work engagement (β = 0.740; p = 0.000) and that engagement in turn lifts performance (β = 0.859; p = 0.000), whereas training shows no significant direct link to performance (β = 0.029; p = 0.693). Because the indirect path is significant (0.636; p = 0.000) while the direct path is not, engagement operates as a full mediator. The evidence indicates that spending on training does not raise performance by itself unless the firm first cultivates a strong sense of engagement among its people.
Compensation, Work-Life Balance, and Turnover Intention: Evidence from Digital Talent in IT Managed Services Eryk Danil Saputro; Fariz Fariz
The Eastasouth Management and Business Vol. 5 No. 01 (2026): The Eastasouth Management and Business (ESMB)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esmb.v5i01.1117

Abstract

This study examines how compensation and work-life balance shape turnover intention among contractual digital talent in the IT managed service division of PT. AAA, with job satisfaction positioned as a mediating mechanism. The research responds to a retention problem in a high-pressure service environment characterized by client-site deployment, shift work, and strict service-level agreement demands. A quantitative causal design was applied to 182 operational employees selected through simple random sampling. Data were collected using a structured Likert-scale questionnaire and analyzed through partial least squares structural equation modeling with a reflective-reflective hierarchical component model and a disjoint two-stage approach. The results show that compensation and work-life balance significantly increase job satisfaction, while job satisfaction significantly reduces turnover intention. However, compensation and work-life balance do not directly reduce turnover intention. Job satisfaction fully mediates both relationships, indicating that retention policies become effective only when they are translated into positive daily work evaluations. The study contributes to digital talent management by clarifying the psychological pathway through which rewards and work arrangements influence retention in B2B managed services.
The Effect of Work Discipline and Work Motivation on Employee Performance: A Case Study at The Regional Secretariat of Southeast Sulawesi Province Valintia Monika; Ratri Wahyuningtyas
The Eastasouth Management and Business Vol. 5 No. 01 (2026): The Eastasouth Management and Business (ESMB)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esmb.v5i01.1127

Abstract

Employee performance is a crucial factor in achieving organizational goals, particularly in government agencies that are required to provide effective and high-quality public services. Performance is influenced not only by employees’ ability to perform their duties but also by work discipline and work motivation, where discipline ensures compliance with rules and responsibilities, while motivation drives employees to perform optimally. This study aims to analyze the effect of work discipline and work motivation on employee performance at the Southeast Sulawesi Provincial Secretariat using a quantitative approach with data collected from 210 respondents through questionnaires. Data analysis was conducted using Structural Equation Modeling-Partial Least Squares (SEM-PLS) with SmartPLS 4, including evaluation of both the measurement model (outer model) and structural model (inner model). The results show that both work discipline and work motivation have a positive and significant effect on employee performance, with work motivation exerting a stronger influence. Descriptively, work discipline is categorized as very high (87.71%), work motivation as high (76.98%), and employee performance as good (82.38%), indicating that improvements in discipline and motivation contribute to better employee performance. The findings imply that organizations should continuously strengthen work discipline and motivation, maintain well-performing aspects, and improve weaker areas through proportional workload management, clear career development systems, and a conducive work environment, while reinforcing teamwork and leadership commitment to support sustainable performance improvement.
Adoption of a Machine Learning-Based Predictive Sampling Module in Retail Productive Credit Audits: A Modified UTAUT Model with Trust Deni Hendra Permana; Ratih Hendayani
The Eastasouth Management and Business Vol. 5 No. 01 (2026): The Eastasouth Management and Business (ESMB)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esmb.v5i01.1128

Abstract

Advances in Artificial Intelligence (AI) and machine learning have driven the use of data-driven systems in the audit process. One such implementation is the Machine Learning Predictive Model Information System (SIMA-MPML), which is used by Bank X’s Internal Audit department to support the selection of audit samples for retail productive loans. This study aims to analyze the factors influencing the acceptance and use of SIMA-MPML by auditors using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model that incorporates the Trust variable. The study employs a quantitative approach using a survey method targeting auditors who use SIMA-MPML. Data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS). The results indicate that Performance Expectancy, Effort Expectancy, Social Influence, and Trust have a positive and significant effect on Behavioral Intention. Additionally, Facilitating Conditions and Behavioral Intention have a positive and significant effect on Use Behavior. Social Influence is the variable with the greatest influence on Behavioral Intention. The results of the mediation test indicate that Behavioral Intention significantly mediates the effects of Performance Expectancy, Effort Expectancy, Social Influence, and Trust on Use Behavior. These findings suggest that the use of SIMA-MPML is influenced by perceived benefits, ease of use, workplace support, trust in the system, and organizational support. This study reinforces the relevance of the UTAUT model in explaining the adoption of machine learning-based technology in internal audit environments and highlights the importance of Trust as a factor supporting the acceptance of Artificial Intelligence-based technology.
The Effect of Social Media Marketing and Email Marketing on Behavioral Intention to Use Gojek: The Mediating Role of Customer Engagement Gibran Al Muzzakir; Muhammad Khairul Amal; Leonita Siwiyanti
The Eastasouth Management and Business Vol. 5 No. 01 (2026): The Eastasouth Management and Business (ESMB)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esmb.v5i01.1137

Abstract

The rapid development of digital technology has increased competition among online transportation service providers, requiring companies to implement effective digital marketing strategies to maintain customer relationships and encourage continuous service usage. This study aims to analyze the effect of Social Media Marketing and Email Marketing on Behavioral Intention to Use with Customer Engagement as a mediating variable among Gojek users in Sukabumi City. This research uses a quantitative approach with an explanatory research design. Data were collected through questionnaires from 120 Gojek users selected using purposive sampling. The data were analyzed using Structural Equation Modeling (SEM) with AMOS. The results show that Social Media Marketing has a significant effect on Customer Engagement, and Email Marketing also has a significant effect on Customer Engagement. Furthermore, Social Media Marketing and Email Marketing significantly influence Behavioral Intention to Use. Customer Engagement also has a significant relationship with Behavioral Intention to Use, although the direction of the relationship is negative. The findings indicate that digital marketing strategies play an important role in shaping users’ intention to continue using online transportation services. This study contributes to digital marketing literature by highlighting the role of Customer Engagement in explaining the relationship between marketing activities and consumer behavioral intentions.

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