Riyaz Abdullah Sheikh

تُعد أنظمة المعلومات الصحية (HIS) أحد المكونات الرئيسية تُعد نظم المعلومات الصحية (HIS) أحد المكونات الرئيسة للرعاية الصحية المعاصرة، إذ تتيح دمج معلومات المرضى واتخاذ القرارات السريرية وتعزيز الكفاءة بشكل سلس. ومع النمو في أحجام البيانات، شهدت أنظمة الرعاية الصحية تحولات جوهرية بفضل استخدام تحليلات descriptive, predictive, prescriptive, discovery-based. وتهدف هذه الورقة إلى تقديم مناقشة مفصلة لتاريخ HIS، وأكثر تقنيات analytics شيوعًا التي تم تطبيقها، وكيف استُخدمت هذه الأدوات لتحسين البحث السريري، ورعاية المرضى، وتقديم خدمات الرعاية الصحية، وإدارة القوى العاملة. كما تُحلل الورقة الفجوات البحثية في الأدبيات، وتستعرض الاتجاهات الحالية، وتناقش أهم الممكنات التكنولوجية، وهي blockchain، AI/ML، cloud computing، big data. إضافة إلى ذلك، تتناول المراجعة عوائق التبني، إلى جانب الآثار المترتبة على البحث والممارسة في المستقبل تُبرز هذه المراجعة أهم الاتجاهات والعوامل التكنولوجية المُمكِّنة والعوائق المرتبطة بالتبنّي، وتُؤكّد أن معالجة هذه التحديات أمرٌ أساسي للاستفادة الكاملة من HIS وanalytics في تحقيق الأهداف الصحية العالمية، ولا سيما الهدف الثالث من أهداف التنمية المستدامة (SDG 3).

This study investigates how artificial intelligence (AI) capabilities shape sustainable entrepreneurship (SE) among small and medium-sized enterprises (SMEs) in emerging economies. Focusing on knowledge management (KM) as a mediator, entrepreneurial orientation (EO) as a moderator, and government policy support (GPS) as an enabler, the research draws upon the Knowledge-Based View, Dynamic Capabilities Theory, and Institutional Theory. Using data from Saudi Arabian SMEs operating within the Vision 2030 agenda, the structural model demonstrates that AI primarily influences sustainability when firms possess robust KM systems capable of translating digital insights into actionable practices. Both EO and GPS strengthen the conversion of knowledge into sustainable outcomes, where EO fosters innovation and proactivity, and GPS provides essential resources and legitimacy. Nevertheless, excessive reliance on policy incentives may divert firms toward compliance rather than substantive transformation. Conceptually, this paper situates KM at the core of sustainability transformation, with policy support shaping the institutional context. The findings offer actionable guidance for SME managers and policymakers seeking to advance the United Nations Sustainable Development Goals (SDGs) through strategic engagement with AI and KM.

The present research explores how sustainability practices affect customer relationship quality in retail stores. The study explores factors like sustainable product recommendations, smart energy efficiency displays, green certification, and eco-friendly packaging and their effect on customer engagement, customer perception, and loyalty. Using a cross-sectional approach, data was collected from 745 retail customers through structured surveys to understand their views on sustainability initiatives and their behavior patterns. The study employs Structural Equation Modelling-Partial Least Squares (SEM-PLS) using SMART PLS4 for data analysis laying the foundation of the study on Expectation-Confirmation Theory (ECT). The findings reveal that sustainable retail practices enhance customer interactions and highlight the importance of consumer perceptions in evaluating the effectiveness of these initiatives making it significant to improve customer relationship quality, leading to better business performance and increased loyalty. The significance of the study extends to its contribution to the United Nations Sustainable Development Goals (SDGs) by promoting responsible consumption and production (SDG12), partnerships for the Goals SDG 17, and industry, Innovation, and Infrastructure (SDG9). The conceptual framework built on ECT and architected by SEM-PLS aligns with SDG making the study highly relevant and novellas it integrates advanced sustainability practices with customer relationship quality, addressing a critical gap in the existing literature.

Purpose: The recent COVID-19 has obliged governments to enact large-scale policies to contain it. A topic of economic debate is the quantification of the impact that these policies can create in the economy, with the aim of activating regulatory mechanisms to minimize this impact. In this vein, this study aims to propose a quantification of the effects of the Italian government policy that blocks nonessential production activities.Design/methodology/approach: The authors use a multisectoral extended inoperability model on the social accounting matrix of Italy. The analysis identifies the pandemic’s impact on outputs, endogenous demands, value-added and disposable incomes of institutional sectors.Findings: The construction and real estate sectors revealed a significant contraction followed by the retail trade and hotel and catering services sectors. The output contraction further impacts the value-added generation, disposable income and final demand components.Originality/value: The current pandemic is alleged to have a greater impact than the epidemics of the past century, considering the present dimension of the world economy and the increasing interconnections between industries and institutions. In this scenario, it is challenging to safeguard not only human health and life but also the economy. Hence, there is a need to establish a trade-off between health and economics; and in this regard, the current study empirically quantifies the impact of health measures on the economy. The findings of this study help identify the sectors that are more prone to disaster effects and also present the structure of income circular flow in the Italian economy.

Purpose: In spite of the popularity of learning analytics (LA) in higher education institutions (HEIs), the success rate and value gained through LA projects is still little and unclear. The existing research on LA focusses more on tactical capabilities rather than its effect on organizational value. The key questions are what are the expected benefits for the institution? And how the investment in LA can bring tangible value? In this research, the authors proposed a value realization framework from LA extending the existing framework of information technology value.Design/methodology/approach: The study includes a detailed literature review focusing on the importance, existing frameworks and LA adoption challenges. Based on the identified research gap, a new framework is designed. The framework depicts the several constructs and their relationships focusing on strategic value realization. Furthermore, this study includes three case studies to validate the framework.Findings: The framework suggests that leveraging LA for strategic value demands adequate investment not only in data infrastructure and analytics but also in staff skill training and development and strategic planning. Universities are required to measure the strategic role of LA and spend wisely in quality data, analytical tools, skilled staff who are aware of the latest technologies and data-driven opportunities for continuous improvement in learning.Originality/value: The framework permits education leaders to design better strategies for attaining excellence in learning and teaching, and furnish learners with new data to settle on the most ideal decisions about learning. The authors believe that the appropriation of this framework and consistent efficient interest in learning analytics by the higher education area will prompt better results for learners, colleges and more extensive society. The research also proposes two approaches and eleven research agendas for future research based on the framework. The first is based on the constructs and their relationships in LA value creation, whereas the later one focusing on identifying problems associate with it.