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Current issue Volume 8, Issue 2 (March-April 2024)


  • A Study on Growth and Venture Capital Financing in India
    Original Research Article
    Country INDIA
  • Pages 01-05
  • Dr. Mohan Megavath
  • Abstract | pdf Pdf
  • Starting a business is a challenging endeavour. An entrepreneur needs to deal with a lot of changes. The first funding needed for a company endeavour has always presented a greater difficulty for the companies. Venture capital has historically been a major source of funding for new businesses. Venture capital is money given to new businesses with the hope of helping them grow over time and in the long run because of their special business strategy. Investment in companies that require equity capital allows venture capitalists and venture capital funds to generate revenue. Venture capital funds will recognize these companies and offer expansion and financial support to ensure the business is executed successfully. Venture capital is becoming one of the most important resources for newly founded, creative businesses. It is associated to an increased risk factor. The paper explains how to comprehend the venture capital phenomenon in India.

    • Determinants of Gen Z employee voice on social media
      Original Research Article
      Country Vietnam
    • Pages 06-20
    • Nguyen Minh Hoa || Pham Thi Khanh Linh || Dao Trang Anh || Dao Thu Trang || Vu Quyet Thang || Dang Ngoc Thanh Huong
    • Abstract | pdf Pdf
    • This article investigates the factors that prompt Gen Z employees to express their dissatisfaction with their company on social media. Qualitative research, including in-depth interviews, and social media were conducted to collect data related to the complaint behavior of students and develop a survey questionnaire. After that, an online survey was conducted with 300 Gen Z participants working in different companies in Hanoi, Vietnam. Data from 275 usable surveys were analyzed by SPSS and AMOS software and a series of statistical techniques to identify determinants of Gen Z employee voice on social media. The research results show that four variables, including three individual factors - self-realization, individualism, job insecurity, and one work-related factor – task performance significantly affect Gen Z employee voice on social media. Implications for company managers to better understand and manage Gen Z employee behavior are discussed.


    • Corporate Social Responsibility, Operating Environment, Employee Empowerment: A Causal Model on Business Performance of Mining Companies in Region XI
      Original Research Article
      Country Philippines
    • Pages 21-39
    • MARY JENNIFER LAORDEN ODIAS || WILLIAM T. SUCUAHI, CPA
    • Abstract | pdf Pdf
    • This study aimed to identify the best-fit model for business performance among 400 managers, supervisors, and lead personnel within mining companies in Region XI. The study assessed the relationship between the exogenous variables, namely corporate social responsibility, operating environment, and employee empowerment, and the business performance as endogenous variable. The research utilized a standardized survey instrument using a quantitative, non-experimental design and a descriptive-correlational technique. The data collection process employed a stratified random sampling technique through both face-to-face and online methods. Various statistical analyses, including Pearson product-moment correlation, mean, and structural equation modeling, were employed to fulfill the study's objectives. The study's findings show that operating environment, corporate social responsibility, and business performance exhibited a very high result, while employee empowerment got a high result. Furthermore, all three exogenous variables were significantly associated with the endogenous variable of business performance. Among the different models assessed, Model 5 emerged as the best-fit model. This model highlighted corporate social responsibility, focusing on legal and ethical aspects, the operating environment considering industry regulation and competition, and employee empowerment, emphasizing meaningfulness and competence. Notably, profit and sales growth indicators represented the business performance model, which remained integral to the model's structure.


    • The Influence of Tax Planning on Company Profits
      Original Research Article
      Country Indonesia
    • Pages 40-48
    • Mahagiyani || Finuri Ulya Mufida Akmalia || Sugiono
    • Abstract | pdf Pdf
    • Taxes are contributions from the people to the state treasury which are indirect and used for state expenditure. Laws and regulations that apply to taxpayers. Companies usually carry out tax planning to reduce costs and as a step in implementing profit management. The Company seeks tax planning to reduce tax costs which affect the Company's profit for the period. Profit information in financial reports is generally important, especially for those who use financial reports for contractual purposes and making investment decisions. This data collection method uses interviews and literature studies. Literature study was carried out with company documents related to research. The results of this research show that tax planning activities influence company profits because tax is one of the factors reducing company profits. Tax planning efforts in companies also require increased efforts so that the results can be maximized.


    • Node Classification Algorithms in Complex Networks Using Graph Embedding
      Original Research Article
      Country China
    • Pages 49-69
    • MUSTAFA MAHYOUB SAEED MOHAMMED || SOROUSH AQA MAHDI
    • Abstract | pdf Pdf
    • This research introduces a novel approach to node classification in complex networks, aiming to enhance accuracy and adaptability across diverse network applications. The Dual Autoencoder Learning Method for Attribute Network Representation is proposed, leveraging two autoencoder channels to capture both structural and attribute information. The first channel uses a multi-hop attention mechanism to incorporate local and global structural aspects, while the second channel employs low-pass filtering to extract attribute information guided by structural characteristics. The innovative fusion process integrates representations from both channels, addressing limitations in existing methods. Experimental validation demonstrates superior performance compared to existing algorithms, highlighting the potential of this approach to improve node classification accuracy and adaptability. This research contributes to advancing graph embedding and node classification techniques, providing a foundation for further exploration in dynamic network environments.