Yazar "Daim, Tugrul" seçeneğine göre listele

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    An Evaluation Framework for Machine Learning and Data Science-Based Financial Strategies: A Case Study-Driven Decision Model
    (Ieee-Inst Electrical Electronics Engineers Inc, 2025) Saadatmand, Mohammadsaleh; Daim, Tugrul; Mena, Carlos; Yalcin, Haydar; Bolatan, Gulin; Chatterjee, Manali
    Big data and computational technologies are increasingly important worldwide in asset and investment management. Many investment management firms are adopting these data science (DS) methods and technologies to improve performance across all investment processes. A good question is whether we can make better decisions in developing quantitative strategies. Therefore, the main objective of this research was to develop a multicriteria assessment framework and scoring decision support system to evaluate quantitative investment strategies that apply machine learning (ML) and DS techniques in their research and development. Subject matter experts will assess all framework perspectives from a systematic literature review to approve their reliability. The perspectives consist of economic and financial foundations, data perspective, features perspective, modeling perspective, and performance perspective. The research methodology applied was the hierarchical decision model (HDM) to provide a 360 degrees view of the quantitative investment strategy and improve and generalize the concept to other asset classes and regions. This study accomplished a rigorous integration of an extensive literature review connecting DS, ML, and investment decision-making in developing quantitative investment strategies. As a result, the major contribution of this study is the comprehensive examination, which included identifying and quantifying perspectives and criteria. The results, while limited indicated significant gaps in strategies examined and therefore generated critical knowledge to improve ML/DS-driven investment strategies, which are valuable for financial companies and policymakers.
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    Artificial Intelligence Usefulness Effect on Business Performance with Trust
    (Springer International Publishing Ag, 2024) Guven, Samet Batuhan; Bolatan, Gulin Idil S.; Daim, Tugrul
    The objective of this research is to examine the impact of AI usage on business performance in organizations. It focuses on assessing trust in AI, perceived usefulness of AI, and the ability of AI-enabled organizations to evaluate business performance. An online survey with 195 middle and senior executives, and engineers was conducted, and data was analyzed using SPSS AMOS software. Three hypotheses were analyzed: trust in AI's positive relationship with job performance, perceived usefulness of AI's positive link with trust in AI, and perceived usefulness of AI's positive association with job performance. Results support the second and third hypotheses, but not the first. This study enhances understanding of AI's impact on business performance. It provides insights into businesses' attitudes towards AI technologies and their effects on organizational performance by examining trust in AI, perceived usefulness of AI, and the ability to evaluate business performance. The findings highlight the positive influence of perceived usefulness of AI and trust in AI on business performance. The study emphasizes the importance of adopting and implementing AI technologies to enhance business performance. AI's capabilities, such as data analysis, prediction, learning, and automated decision-making, optimize operations and achieve faster, more efficient outcomes. Measuring business performance when utilizing AI is significant. AI-based predictive models improve demand forecasting, inventory management, customer experience, and personalization, leading to cost savings, time efficiency, and enhanced quality. Overall, this research offers valuable insights for managers and decision-makers, guiding the adoption and effective utilization of AI technologies in organizations. Understanding AI's role in business fosters research and development efforts, contributing to future innovations in the field.
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    Embracing human centric marketing for enhanced firm performance: the role of innovation strategy in India
    (Emerald Group Publishing Ltd, 2025) Sharma, Mahak; Singh, Pratibha; Bolatan, Gulin; Daim, Tugrul
    PurposeThe purpose of this study is to provide valuable insights to academics as well as practitioners looking for ways to improve their firm performance by judiciously focusing on marketing strategy, i.e. Marketing 5.0.Design/methodology/approachUsing a mixed-method approach, a cross-sectional survey is conducted using 396 responses from an emerging economy setting. Analysis was conducted using a sequential research design, where qualitative interviews are conducted before the quantitative phase.FindingsThe findings suggest that firms should focus on building the right strategies using Marketing 5.0, thereby enhancing technology capability and absorptive capacity to promote open and eco-innovations.Originality/valueThis paper offers integrative and holistic implications to firms looking for ways to improve their overall performance using open and eco-innovations.
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    Evaluating customer orientation in e-commerce: an organization focused technology assessment
    (Routledge Journals, Taylor & Francis Ltd, 2025) Zarrin, Soheil; Daim, Tugrul; Gillpatrick, Tom; Bolatan, Gulin; Sharma, Mahak
    This research focuses on designing a new maturity model to evaluate and plan an organization's customer-centricity. The Hierarchical Decision Model (HDM) is used as the primary methodology to quantify impacting factors and intensity of influence on the ultimate outcome. To demonstrate the proposed model in the real world, a case study is performed in the e-commerce industry, especially B2C online retailer organizations. This research tackles the scarcity of documentation on creating maturity models that are widely accepted and sustainable, demonstrating the innovative use of the HDM methodology. In summary, the study successfully achieves its overarching objective of crafting a comprehensive model for assessing organizational maturity in the customer-centric approach, encouraging the widespread adoption of the proposed methodology across diverse sectors. This study endeavors to introduce an innovative approach for assessing an organization's customer-centricity, incorporating both product and service deliverables, specifically referred to as Product-Service Systems (PSS). The primary objective of this research is to formulate a novel maturity model, enabling organizations to 1) assess their degree of customer-centricity and 2) receive actionable recommendations aimed at enhancing customer orientation and elevating their customer-centricity maturity level..
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    Strategic conceptualization and operationalization of digital orientation to support organizational TQM performance
    (Routledge Journals, Taylor & Francis Ltd, 2024) Raj, Rohit; Kumar, Vimal; Bolatan, Gulin Idil; Daim, Tugrul
    A digital orientation in TQM performance brings about numerous advantages, including data-driven decision-making, enhanced efficiency, flexibility, improved customer experience, continuous improvement, effective communication, competitive advantage, and stimulated innovation. This research focuses on bridging the knowledge gap by exploring the connection between organizational TQM performance and the antecedents of digital orientation, considering the lack of empirical research in this domain. Using data gathered from surveys of 310 small to medium-sized IT companies in India, the study evaluates a novel conceptual framework through structural equation modeling (SEM) analysis employing the partial least squares (PLS) technique. The results underscore the importance for businesses to seize the opportunity presented by digital technologies and the ongoing trend of digitalization in their industries. This involves committing to adopting these technologies, taking into account their maturity and intensity, and enhancing digital capabilities to foster resourcefulness and creativity, thereby improving organizational TQM performance. By providing a deeper understanding of pertinent antecedents of digital orientation and how they should be strategically deployed within organizations, this research contributes to the body of knowledge on organizational TQM performance and digital-oriented solutions. The findings reveal a positive impact of digital maturity, digital intensity, and digital ecosystem transformation on organizational TQM performance. Additionally, it is demonstrated that the relationship between organizational TQM performance and digital capability is mediated by digital ecosystem transformation.
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    Technology readiness assessment: Case of clinical decision support systems in healthcare
    (Elsevier Sci Ltd, 2024) Laraichi, Oussama; Daim, Tugrul; Alzahrani, Saeed; Hogaboam, Liliya; Bolatan, Gulin Idil; Moughari, Mahdieh Mokthtari
    Clinical Decision Support Systems (CDSS) play a critical role in modern healthcare by supporting healthcare providers in making well-informed decisions, improving patient safety and outcomes, enhancing efficiency, and promoting evidence-based practices. Their integration into clinical workflows can lead to more effective and patient-centered care. CDSS is essential tools for healthcare organizations as well as for healthcare providers to improve clinical care. However, successful implementation of CDSS can be challenging. Therefore, before implementing CDSS, it is crucial to assess the readiness of healthcare organizations to implement these tools. CDSS is essential tools in healthcare for several compelling reasons. For instance, enhanced patient safety, improved diagnostic accuracy, optimized treatment plans, consistency in care, and support for complex decisions. This study's aim is to develop a model that will help healthcare organizations identify the challenges of implementing CDSS, and to assess their readiness for such an implementation in a comprehensive and multidimensional manner. Through a literature review, the first step of this research explores the concept of clinical decision support and CDSS, discussing their features, characteristics, and organizational hurdles to implementation. It also provides perspectives on CDSS adoption in the context of information systems and health technology. The review helped to identify research gaps, objectives, and questions. To address these gaps and to attempt to answer the research questions, a Hierarchical Decision Model (HDM) is proposed. The model allows us to assess the readiness of healthcare organizations for CDSS implementation. It presents four perspectives and sixteen criteria for a multi-dimensional assessment. The methodology involves expert panels for the HDM model's refinement, validation, and quantification. Two case studies are then presented to demonstrate the HDM model's application to identify real-world CDSS implementation challenges and to provide insights and recommendations. The research contributions are evaluated against the identified gaps in the literature review, with limitations and future research presented. In conclusion, this research provides valuable insights into CDSS implementation readiness assessment and highlights the need for careful consideration and planning. The proposed HDM model offers a valuable framework for healthcare organizations to evaluate their readiness for CDSS implementation.
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    Uncovering the Critical Drivers of Blockchain Sustainability in Higher Education Using a Deep Learning-Based Hybrid SEM-ANN Approach
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) AlShamsi, Mohammed; Al-Emran, Mostafa; Daim, Tugrul; Al-Sharafi, Mohammed A.; Bolatan, Gulin Idil S.; Shaalan, Khaled
    The increasing popularity of blockchain technology has led to its adoption in various sectors, including higher education. However, the sustainability of blockchain in higher education is yet to be fully understood. Therefore, this research examines the determinants affecting blockchain sustainability by developing a theoretical model that integrates the protection motivation theory and expectation confirmation model. Based on 374 valid responses collected from university students, the proposed model is evaluated through a deep learning-based hybrid structural equation modeling (SEM) and artificial neural network approach. The partial least squares-SEM results confirmed most of the hypotheses in the proposed model. The sensitivity analysis outcomes discovered that users' satisfaction is the most important factor affecting blockchain sustainability, with 100% normalized importance, followed by perceived usefulness (58.8%), perceived severity (12.1%), and response costs (9.2%). The findings of this research provide valuable insights for higher education institutions and other stakeholders looking to sustain the use of blockchain technology.