International Multidisciplinary Journal Of Science, Technology & Business
The International Multidisciplinary Journal Of Science, Technology & Business (IMJSTB) is a peer-reviewed, open-access journal that publishes high-quality original research papers, review articles, and short communications in the fields of science, technology, and business. The journal covers a wide range of topics, including but not limited to: Mathematics and statistics Computer Science and Information Technology (AI, Data Science, BI) Physics and Chemistry Biology and Life Sciences Engineering and Technology Business and Management Economics and Finance Social Sciences
IAQ CENTER OF EXCELLENCE IN RESEARCH, LAHORE, PAKISTAN
Smart Contracts and Corporate Contracting: Legal, Operational, and Managerial Challenges
The rapid pace of digital transformation has fundamentally reshaped the landscape of corporate contracting. Among the most disruptive innovations are smart contracts—self-executing digital agreements encoded on block chain networks that automatically enforce predefined terms. Their promise is profound: increased efficiency, transparency, and automation in enterprise operations. Yet, as organizations seek to integrate smart contracts with core business processes, including Enterprise Resource Planning (ERP) systems, and aim for robust audibility, a host of legal, operational, and managerial challenges emerge. These challenges intersect profoundly with human resources and organizational behavior, raising questions about enforce ability, technological integration, work practices, and managerial accountability.
Copyright (c) 2026 International Multidisciplinary Journal Of Science, Technology & Business
The Business Value of Causal Inference Versus Prediction in Managerial Decision-Making: Quantifying the Impact in Marketing, Pricing, and Operations
Managers routinely rely on predictive models to forecast customer behavior, demand, and operational metrics. However, many strategic and tactical decisions—such as setting prices, designing promotions, or reallocating capacity—require understanding how actions causally affect outcomes. This paper quantifies the business value of causal inference relative to prediction across three core managerial domains: marketing, pricing, and operations. We develop a unified decision-theoretic framework that maps model accuracy and causal identification quality into expected profit increments under realistic constraints (budget, competition, and operational friction). Using a combination of analytic derivations and calibrated simulations based on industry data, we show that prediction-focused models can perform well when decision rules are passive or optimization is over naturally exogenous variation, but they systematically underperform when policies require counterfactual estimation (e.g., evaluating new prices, treatment roll out, or capacity shifts). In marketing, causal estimates of incremental response to promotions yield higher return on ad spend and reduce overspending on ineffective segments; in pricing, causal demand elasticity enable more profitable dynamic pricing and reduce revenue loss from biased price experiments; in operations, causal models of process changes and bottleneck interventions lead to greater throughput gains and lower service-level violations than predictive proxies. We provide closed-form expressions for the value of information in canonical settings and derive bounds on expected profit loss when using predictive instead of causal models as a function of confounding bias, heterogeneity, and decision leverage. Finally, we present practical guidelines for managers on when to invest in causal-data collection (experiments, instrumental variables, panel methods) versus enhancing predictive performance, and propose a cost–benefit procedure to prioritize causal initiatives. Our results demonstrate that the business value of causal inference is substantial and context-dependent: firms with high decision leverage and heterogeneous responses gain the most from causal approaches, while low-leverage, stable environments may rely more safely on prediction.
Strategic Alignment of IT Investments and ESG Targets: Integrating Data, Analytics, and Decision-Making for Sustainable Corporate Transformation
The accelerating digitization of the global economy has placed information technology (IT) at the core of corporate strategy, not only as a driver of efficiency and innovation but also as a critical enabler of environmental, social, and governance (ESG) objectives. In the contemporary business landscape, ESG targets have evolved from peripheral concerns into central strategic imperatives, driven by regulatory pressures, stakeholder expectations, and the pursuit of long-term value creation. The convergence of IT investments with ESG ambitions is no longer optional; it is a necessity for firms seeking sustainable growth, social legitimacy, and resilience in the face of rapid technological and societal change (Pastor-Escuredo, 2021; Peppiatt, 2024).
Digital Nomadism and Remote-First Organizational Design: Productivity and Culture Trade-Offs
The rapid acceleration of digital technologies and the proliferation of remote work have fundamentally transformed the landscape of modern entrepreneurship and small-to-medium enterprises (SMEs). Digital nomadism—the practice of leveraging digital tools to work remotely from any location—and remote-first organizational design have shifted traditional notions of productivity, innovation, and workplace culture. The COVID-19 pandemic catalyzed a global experiment in remote work, compelling organizations of all sizes to reconsider their operational models (Pastor-Escuredo, 2021). While these shifts offer significant benefits in flexibility and access to a global talent pool, they also pose challenges to innovation, cohesion, and ethical management.
Beyond the Peter Principle: A Taxonomy of Organizational Incompetence
This study establishes a taxonomy identifying nine types of organizational incompetence across five dimensions: frequency, impact, persistence, mitigation difficulty, and cross-sector presence. Responding to limitations in existing single-dimension frameworks, the taxonomy reveals hierarchical relationships wherein structural and institutional incompetence (accounting for 58% of implementation failures) serve as foundational dysfunctions amplifying downstream effects by 25-35%. Case studies across manufacturing, healthcare, and technology sectors demonstrate that addressing foundational dysfunctions produces cascading organizational improvements.
Copyright (c) 2025 International Multidisciplinary Journal Of Science, Technology & Business
The accelerating digitalization of the global economy has placed information technology (IT) at the core of corporate strategy, not only as a driver of efficiency and innovation but also as a critical enabler of environmental, social, and governance (ESG) objectives. In the contemporary business landscape, ESG targets have evolved from peripheral concerns into central strategic imperatives, driven by regulatory pressures, stakeholder expectations, and the pursuit of long-term value creation. The convergence of IT investments with ESG ambitions is no longer optional; it is a necessity for firms seeking sustainable growth, social legitimacy, and resilience in the face of rapid technological and societal change (Pastor-Escuredo, 2021; Peppiatt, 2024).
The rapid acceleration of digital technologies and the proliferation of remote work have fundamentally transformed the landscape of modern entrepreneurship and small-to-medium enterprises (SMEs). Digital nomadism—the practice of leveraging digital tools to work remotely from any location—and remote-first organizational design have shifted traditional notions of productivity, innovation, and workplace culture. The COVID-19 pandemic catalyzed a global experiment in remote work, compelling organizations of all sizes to reconsider their operational models (Pastor-Escuredo, 2021).
Personalization vs. Privacy: Optimizing Customer Lifetime Value under Evolving Privacy Regulations
The rapid advancement of artificial intelligence (AI), machine learning, and data-driven automation has transformed the landscape of customer relationship management, enabling unprecedented levels of personalization in products, services, and digital interactions. However, these developments have also raised profound concerns about privacy, data protection, and the ethical use of personal information, especially in light of evolving global privacy regulations. Organizations are thus confronted with a fundamental tradeoff: how to optimize customer lifetime value (CLV) through personalization while maintaining compliance and trust under increasingly stringent consent and privacy constraints.
Automation, Workforce Redesign, and the Future of Work in Manufacturing: Skill Shifts, Retraining, and Productivity Impacts
The manufacturing sector stands at the nexus of technological innovation and labor transformation, making it a critical case study for understanding the impacts of automation, workforce redesign, and the future of work. The rapid proliferation of digital technologies—artificial intelligence (AI), robotics, Internet of Things (IoT), and advanced data analytics—has propelled a new era of intelligent automation, raising profound questions about the nature of work, skill requirements, training needs, and productivity within manufacturing and related industries (Amenyo, 2018; Pastor-Escuredo, 2021). While previous industrial revolutions have also been characterized by labor-saving technologies and job restructuring, the scale, scope, and velocity of current changes, particularly with the advent of generative AI, distinguish the present transition (Peppiatt, 2024; Frank, 2023).
Resilient Supply Chain Design Using Digital Twins and Real-Time Data: Examining ROI and Decision Speed Improvements
In an era marked by global disruptions, geopolitical instability, and rapidly changing consumer demands, the resilience of supply chains has emerged as a critical determinant of organizational success. The COVID-19 pandemic, natural disasters, and supply shocks have exposed vulnerabilities in traditional supply chain models, underscoring the urgent need for adaptive, robust, and agile systems. Digital transformation, particularly through the adoption of digital twins and the integration of real-time data, promises to revolutionize supply chain design and management, enhancing both return on investment (ROI) and decision speed.
Open Innovation and Crowdsourcing Platforms: Governance Models that Ensure Quality and IP Protection
Open innovation and crowdsourcing platforms have become pivotal in harnessing collective intelligence, accelerating innovation, and solving complex problems across industries. By inviting contributions from diverse and distributed communities, these platforms promise unparalleled creativity, speed, and scale. However, the open nature of crowdsourcing introduces significant challenges in ensuring the quality of outputs and protecting intellectual property (IP) rights. Effective governance models are crucial to mitigate these challenges, balancing the incentives that attract contributors, the moderation systems that ensure quality, and the legal mechanisms that safeguard IP.
Managing the Product Lifecycle for AI-Enabled Products: From Prototype to Responsible Retirement
Artificial intelligence (AI)-enabled products are transforming industries, supply chains, and operational paradigms at an unprecedented pace. Their lifecycle management—spanning initial prototyping, deployment, continuous maintenance, and responsible decommissioning—presents unique challenges and risks distinct from those of traditional products. Critical issues such as model drift, maintenance costs, and decommissioning must be addressed within a robust lifecycle governance framework to ensure both operational resilience and the responsible stewardship of AI technologies.
Accelerating Innovation through AI-Assisted Design Tools: Productivity and Capability Implications
The accelerating advancement of artificial intelligence (AI) has transformed not only core computational domains but also the landscape of innovation, design, and research and development (R&D). AI-assisted design tools, encompassing a spectrum from generative models for creative tasks to decision-support systems in engineering and science, have emerged as pivotal enablers in contemporary creative workflows. Their promise is twofold: to enhance productivity through automation and augmentation, and to extend the very capabilities of human designers and researchers, enabling innovation that would otherwise be infeasible.
Effectiveness of Data Trust Mechanisms in Enabling Cross-Firm Analytics: Focus on Privacy, Commercial Incentives, and Governance
The digital economy is increasingly characterized by the need for collaboration and data sharing across organizational boundaries. Cross-firm analytics, the practice of conducting joint data analyses between firms, holds immense potential for generating insights that drive innovation, efficiency, and competitive advantage. However, such collaboration is fraught with challenges related to privacy, commercial incentives, and governance. Traditional data sharing approaches often expose firms to regulatory, ethical, and competitive risks, particularly in the era of heightened data protection concerns and evolving artificial intelligence (AI) capabilities (Slattery et al., 2024; Lee et al., 2023).
How Legacy Firms Orchestrate Digital Platform Transitions without Disrupting Core Operations: Strategic Choices, Capability-Building, and Governance
In the era of rapid technological advancement, legacy firms—long-established organizations with deeply entrenched business models and operational processes—are compelled to adapt to the evolving digital landscape. The proliferation of digital platforms, characterized by multi-sided markets, network effects, and data-driven value creation, has fundamentally altered competitive dynamics across industries. For legacy firms, transitioning to digital platforms presents both existential risks and transformative opportunities. However, such transitions are fraught with challenges, particularly the risk of disrupting core operations that underpin the firm’s current value and market position.
Key Performance Indicators (KPIs) and Board Oversight for AI-Driven Decision Systems
The proliferation of Artificial Intelligence (AI) systems across critical sectors has led to a paradigm shift in organizational decision-making, regulatory expectations, and societal risk landscapes. AI-driven decision systems now influence domains as diverse as healthcare, finance, defense, and public policy, embedding themselves deeply into the operational fabric of modern organizations. With this growing integration comes an urgent need for effective governance mechanisms to ensure these systems deliver value while minimizing risks—ethical, operational, and existential. At the forefront of these governance mechanisms are boards of directors, whose oversight responsibilities are increasingly being redefined by the complexities of AI technologies.
Leveraging Low-Code/No-Code Platforms for Rapid Digital Transformation in Small and Medium-sized Enterprises (SMEs)
Low-code/no-code (LCNC) platforms significantly reduce the technical barriers to software development, enabling small and medium-sized enterprises (SMEs) to accelerate digital transformation. This paper examines how SMEs can leverage LCNC platforms to achieve rapid digital transformation. It evaluates the benefits and risks of LCNC adoption and identifies the managerial capabilities required to maximize value and mitigate hazards. The methodology combines multiple case studies with return-on-investment (ROI) analysis across typical SME scenarios. Findings indicate that when supported by proper governance, skills development, and change management, LCNC can produce high ROI through faster time-to-market, reduced development costs, and improved operational agility. However, unmanaged adoption can create security, quality, and vendor-lock-in risks. The paper concludes with practical guidelines for SME leaders, a proposed framework for LCNC adoption, and directions for further resea
low-code, no-code, LCNC, digital transformation, SMEs, ROI, case study, governance, citizen development
The Reputational Impact of AI Incidents: Modeling Downstream Effects on Sales and Firm Valuation
Artificial intelligence (AI) incidents—ranging from biased algorithmic decisions to data breaches and harmful autonomous behavior—can damage firm reputation, potentially reducing sales and market valuation. This paper develops metrics to quantify reputational damage from AI-related scandals and empirically estimates their impact on sales and stock returns. Using a mixed-methods approach, we (1) construct an AI‑Incident Reputation Index (AIRI) combining event severity, media salience, regulatory action, and social sentiment; (2) perform sentiment analysis on news and social media to capture reputation shifts; and (3) estimate short- and long-term financial effects using event study methods for stock returns and panel regressions for quarterly sales. We compile a dataset of 86 AI incidents across public firms from 2016–2024 spanning sectors (technology, finance, healthcare, retail). Results show that severe AI incidents produce statistically significant negative abnormal returns in the short term (cumulative abnormal returns of −3.8% in a [-1,+1] window for high-severity incidents) and persistent sales declines for consumer-facing firms (avg. quarterly sales drop of 2.6% over two quarters). The AIRI and sentiment measures improve explanatory power beyond binary incident indicators. We discuss managerial implications for incident response, disclosure, and reputation management, and provide an event-driven toolkit for researchers and practitioners.
Ecosystem Strategies for Platform-Based Business Models in Regulated Sectors:Partnerships, API Governance, and Regulatory Compliance
Platform business models create value by enabling multi-sided interactions across ecosystems of firms, developers, and users. In regulated sectors (finance, healthcare, energy, telecommunications), platforms must reconcile network effects and openness with stringent regulatory obligations—privacy, safety, consumer protection, financial stability, and sector-specific licensing. This paper examines how platforms in regulated sectors design ecosystem strategies around partnerships, API governance, and compliance. Using comparative case studies and network analysis on three platform ecosystems (a European digital banking platform, a US health‑tech data exchange, and a Southeast Asian energy trading platform), we analyze how governance choices affect diffusion, regulatory exposure, and ecosystem resilience. We synthesize literature on platform strategy, regulation, and interorganizational networks; present methods and empirical results; and propose a contingency framework linking regulatory stringency, platform openness, and governance instruments. Findings show that tighter regulatory environments push platforms toward stronger gatekeeping, contractual controls, and technical enforcement (API throttling, provenance, consent APIs), while permissive or fragmented regimes allow more decentralized partner models but increase compliance uncertainty. We conclude with managerial implications, a prescriptive API governance toolkit for regulated platforms, and suggestions for future research.
Designing Internal Governance, Compliance, and Audit Mechanisms for Responsible AI in Firms: Navigating Evolving External Regulation
Firms deploying AI face legal, reputational, fairness, and trust-related risks that hinder adoption. This paper examines how organizations can design internal governance, compliance, and audit mechanisms to manage AI risks while adapting to changing external regulation. We integrate policy analysis, design science, action research, and comparative case studies across industries and countries to (1) identify effective internal processes (audit trails, red‑teaming, AI impact assessments) for preventing harms, (2) analyze how differing national regulations reshape corporate AI governance strategies, and (3) propose KPIs and board-level monitoring frameworks for AI risk. We present a prescriptive governance design—the Responsible AI Management System (RAIMS)—and evaluate it against industry cases from finance, healthcare, and technology sectors in three jurisdictions (EU, US, Singapore). We conclude with actionable recommendations for firms, policymakers, and auditors.
AI for Strategic Decision‑Making and Organizational Design
This paper examines how artificial intelligence (AI) tools can be integrated into top‑management decision processes and organizational structures without generating harmful dependency, bias, or erosion of human judgment. Drawing on literature from strategic management, organizational design, and human–AI collaboration, I develop theory and hypotheses about three managerial decisions: AI governance structure, use of hybrid human–AI teams, and centralization versus decentralization of AI capabilities. Using simulated cross‑sectional firm‑level data (N = 800 firms across 20 industries) and two‑level multilevel models (firms nested in industries), I illustrate empirical approaches to test the hypotheses. Results from OLS regressions and hierarchical linear models show (simulated) patterns consistent with the theory: stronger internal AI governance (AI governance index) is positively associated with strategic decision quality and reduces the negative association between AI reliance and decision accountability; hybrid teams increase decision speed but their effect on decision quality depends on team design and training; and decentralized AI capabilities improve responsiveness in dynamic environments while centralized capabilities yield higher strategic coherence in stable contexts. I conclude with implications for managers, policy, and future empirical research, provide full R code to reproduce the simulated data and analyses, and discuss limitations of simulation-based inference.
AI in Predictive Analytics: EnhancingDecision-Making in Retail andHealthcare
This paper explores the transformative role of Artificial Intelligence (AI) inpredictiveanalytics, specifically within the retail and healthcare sectors. As organizations increasinglycontendwith vast datasets, AI algorithms provide innovative solutions for forecasting trends, enhancingoperational efficiency, and improving decision-making processes. Through a comparativeanalysisofcase studies from retail and healthcare, we illustrate how AI-driven predictive modelsenablebusinesses to anticipate customer behavior, optimize inventory management, andpersonalizemarketing strategies in the retail landscape. In healthcare, we highlight the applicationof predictiveanalytics in patient outcome forecasting, resource allocation, and disease outbreak prediction. Ourfindings underscore the potential of AI to not only increase accuracy in predictions but alsotofosterproactive strategies that enhance performance and patient care. The paper concludes bydiscussingthe challenges of integrating AI solutions, including data privacy issues and the needforskilledpersonnel, while advocating for future research to develop frameworks that maximize thebenefitsofAI in predictive analytics across sectors
The Evolution of Quantum Computing:Challenges and Future Prospects
A paradigm change in computational technology, quantum computing uses the ideas of quantum physics to tackle unsolvable problems. This paper examines the quantum computing from theory to the present day. It presents the main barriers that prevent practical quantum computers from becoming a reality, being the undesirable effects of decoherence, error correction, making them scale. Finally, the paper also discusses future prospects for application in cryptography, optimization and artificial intelligence. This paper synthesizes existing research with the objective of transcribing the field and directing future work.
Understanding and Mitigating Bias and Noise in Data Collection, Imputation and Analysis
Bias and noise in data significantly impact the accuracy and reliability of research findings and data-driven decision-making. This paper provides a comprehensiveoverview of various types of bias and noise affecting data quality, their impact onresearchand decision-making, and strategies for mitigation. We examine sampling bias, nonresponse bias, measurement bias, imputation bias, and analysis bias, as well as the roleof noise as a source of bias. The paper also explores bias in survey design and interpretation, emphasizing the importance of careful question wording, structure, and considerationof cultural and linguistic factors. To address these issues, we propose several strategies, including appropriate sampling techniques, methods to encourage participation, improved measurement toolsand protocols, suitable imputation methods, and transparent data analysis practices. Wediscuss the ethical implications of biased data and the responsibility of researchers, decision-makers, and institutions to prioritize bias and noise mitigation. The paper concludes by calling for future research on methodological tools for detecting and mitigating bias and noise. It stresses the need for interdisciplinary collaboration to ensure the integrity and trustworthiness of data-driven insights.
The Promise and Perils of Mobile Learning: Impacts on Students' Academic Experience and Work-Life Balance
Mobile learning has become increasingly prevalent in educational settings, providing students with access to educational content through mobile devices. This paper aims to explore the impact of mobile learning on students' academic experience and work-life balance. The research will examine the benefits of mobile learning in enhancing students' academic performance, promoting flexible learning schedules, and fostering independent learning. Additionally, the paper will discuss the potential challenges and implications for students' work-life balance. The findings of this research will provide valuable insights into the role of mobile learning in shaping students' academic experiences and its impact on their overall well-being.
Copyright (c) 2023 International Multidisciplinary Journal Of Science, Technology & Business
Intelligent Disease Prediction System for Hepatitis C Patients
Accurate and timely predictions are essential for minimizing the devastating side effects of medical therapies, especially when dealing with serious illnesses like hepatitis C. In this research, a machine learning-based classification framework has been proposed to enable more reliable predictions of treatment responses in hepatitis C patients. The focus of this framework is to explore the potential of Machine Learning Techniques (MLT) in predicting, forecasting, and treating chronic diseases such as tumors, hepatitis, and heart diseases. In the proposed model, five ML algorithms - Naïve Bayes (NB), Decision Tree (DT), Bayesian Nets (BNs), Support Vector Machine (SVM), and Random Forest (RF) - are employed to predict the outcomes of hepatitis C patients undergoing treatment. The AI-enabled medical services model that has been developed is capable of delivering accurate predictions for hepatitis C with improved results in the future. Furthermore, the framework can be further enhanced by including additional ML techniques, such as deep learning or reinforcement learning, to achieve even more accurate results
The Study on Guerrilla Marketing with Ad Creativity Affects Word of Mouth of Consumers.
This study examines the impact of guerrilla marketing tools, supported by ad creativity, on consumer word-of-mouth. The objective is to explore the role of sensation marketing, characterized by one-time surprise effects, in generating word-of-mouth and leveraging creative advertising media. The study adopts a quantitative approach, employing a closedended questionnaire technique to collect data from 202 respondents. The questionnaire was filled out cross-sectionally by individuals regularly exposed to various forms of advertisements. Hypothesis tests were conducted using the Linear and Hierarchical Regression method in SPSS20.0. The findings confirm the influence of guerrilla sensation on word-of-mouth, with ad creativity mediating the relationship between guerrilla sensation and word-of-mouth. Additionally, the study demonstrates the impact of ad creativity on word-of-mouth. In conclusion, the study suggests that surprise and humor, combined with a clear message, emotionally engage consumers, leading to attention, resistance, and memorization of guerrilla marketing messages. This approach, facilitated by creative advertising media and incorporating novelty and relevance elements, enhances curiosity and promotes effective word-of-mouth, aiding companies in spreading their message. The study also highlights the potential positive or negative impact of word-of-mouth on a company's reputation. Considering ethical considerations in advertising presentation and the digital media revolution, the concept of sensation marketing can be adapted to digital marketing platforms
"International Journal of Innovation in Science, Technology, and Business"
The "International Journal of Innovation in Science, Technology, and Business" is a multidisciplinary platform dedicated to advancing research and knowledge dissemination in the fields of science, technology, and business innovation. This journal seeks to bridge the gap between academia and industry by promoting cutting-edge research and fostering collaboration among scholars, practitioners, and policymakers. In this paper, we provide an overview of the journal's scope, objectives, and contributions, highlighting its significance in the contemporary landscape of innovation and knowledge exchange.
"Advancements at the Intersection: A Journal of Science, Technology, and Business"
Advancements at the Intersection: A Journal of Science, Technology, and Business" is a multidisciplinary publication that explores the dynamic confluence of science, technology, and business in our rapidly evolving world. This journal serves as a platform for scholars, researchers, and practitioners to exchange ideas, showcase innovations, and contribute to the holistic understanding of the intricate relationships between these fields. In this article, we provide an overview of the journal, its key focus areas, and the importance of studying the intersections of science, technology, and business in today's society.
"Exploring the Synergy: Science, Technology, and Business Innovations"
This interdisciplinary research paper delves into the dynamic synergy between science, technology, and business innovations. In an era marked by rapid advancements and globalization, the interplay between these domains has become increasingly crucial for fostering growth, competitiveness, and sustainability. This paper explores the intricate connections, challenges, and opportunities that arise at the intersection of these three fields, shedding light on the transformative potential of their collaboration.