Mohammad T Islam (BSc, MBA, MEd)

English for Academic Purposes (EAP): Concepts, Practices, and Challenges

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English for Academic Purposes (EAP) has developed as a significant field within applied linguistics and English language teaching. It is primarily concerned with preparing learners to use English effectively in academic contexts such as universities, research environments, and professional scholarly communication. EAP differs from General English in that it emphasises academic discourse, disciplinary-specific communication, and the development of critical literacy skills required for higher education. As globalisation and internationalisation of education have increased, EAP has become essential for students navigating English-medium institutions worldwide (Hyland, 2006). This article explores the concepts, practices, and challenges of EAP, highlighting key pedagogical frameworks, contemporary debates, and practical examples, supported by scholarly literature and empirical research. 1.0 Defining English for Academic Purposes EAP is defined as the branch of English for Specific Purposes (ESP) that focuses on the language, skills, and practices necessary for academic study and scholarly communication (Jordan, 1997). While ESP caters to professional or vocational fields (e.g., English for Medical Purposes, English for Business), EAP specifically targets learners engaged in academic study. According to Hyland and Hamp-Lyons (2002), EAP encompasses not just language structures but also the academic literacies students require to participate effectively in higher education. A key feature of EAP is its needs-based orientation. Unlike general English courses, EAP begins with a detailed needs analysis to identify the specific linguistic and cognitive requirements of learners within their disciplines (Dudley-Evans & St John, 1998). For example, science students may need to focus on reading research articles and writing laboratory reports, whereas humanities students may require training in essay writing and critical analysis. 2.0 Pedagogical Approaches in EAP 2.1 Skills-based Approach The skills-based approach emphasises developing the four core skills—reading, writing, listening, and speaking—for academic contexts. This includes critical reading, note-taking, academic writing, and seminar participation (Minarsih, Tyas & Herlina, 2025). 2.2 Genre-based Approach The genre approach, heavily influenced by systemic functional linguistics, examines the textual and rhetorical structures of academic discourse. Hyland (2004) argues that genre pedagogy empowers learners by exposing them to authentic models of academic texts, such as dissertations, journal articles, and conference abstracts. 2.3 Corpus-based Approach Corpus linguistics has enabled teachers to use large databases of academic texts to analyse recurrent patterns in academic writing. For instance, research shows that signalling nouns, hedging, and nominalisation are key features of dissertation writing (Yang, 2025). 2.4 Online and Blended Learning The rise of digital education has transformed EAP delivery. Bao (2025) highlights that EAP MOOCs and online writing programmes have expanded access, though challenges remain in maintaining engagement and providing individualised feedback. 3.0 Critical Literacy and EAP EAP is not limited to teaching grammar or vocabulary but emphasises critical literacy—the ability to question sources, evaluate arguments, and construct academic knowledge (Nguyen & Trinh, 2025). Critical reading strategies are central to EAP courses, equipping students to navigate dense, discipline-specific materials. For example, a student in engineering must critically interpret research data, while a literature student may analyse theoretical frameworks. Wong (2025) provides a critical EAP perspective, noting that EAP is inherently linked to issues of identity, power, and resistance. By teaching academic English, institutions may implicitly promote certain cultural or ideological values, raising concerns about linguistic imperialism. Thus, educators must balance language training with respect for students’ linguistic and cultural backgrounds. 4.0 The Role of EAP Educators EAP practitioners occupy a unique position within universities. They are often seen as support staff rather than disciplinary experts, which may undermine their professional identity. Taylorson, Mavor and Miles (2025) argue that applying a psychological lens reveals the identity challenges faced by EAP educators, who negotiate between language pedagogy and disciplinary authority. Professional development and collaboration with subject specialists can strengthen EAP provision. For example, joint modules between EAP lecturers and faculty members in engineering or business can provide students with both linguistic scaffolding and disciplinary knowledge. 5.0 EAP Across Disciplines A major challenge in EAP is addressing disciplinary variation. Research shows that science textbooks employ different grammatical and lexical structures compared to humanities materials (Yang, Lan & Huang, 2025). Similarly, English for Medical Purposes requires specialised vocabulary, diagnostic communication skills, and familiarity with medical discourse (Aarvidurai, Anandhan & Thiyagarajan, 2025). Therefore, effective EAP must be discipline-sensitive, recognising that academic communication is not homogeneous but shaped by the epistemological norms of different fields. 6.0 Technological Integration in EAP The integration of digital tools has reshaped EAP teaching. Leva et al. (2025) note that translation students increasingly rely on university-provided tools for academic tasks. Similarly, Shalhoub and Kunt (2025) highlight that online distance learning (ODL) during COVID-19 accelerated the adoption of platforms such as Moodle and Zoom for EAP instruction. While these tools increase accessibility, they also pose challenges in terms of digital literacy and the risk of over-reliance on AI-based translation or writing tools, which may undermine the development of independent academic skills. 7.0 Challenges in EAP 7.1 Cultural Diversity – Students from varied linguistic and cultural backgrounds face different barriers to academic integration. 7.2 Assessment – Measuring EAP outcomes remains complex. Traditional exams may not capture skills such as critical thinking or collaborative learning. 7.3 AI and Academic Integrity – With tools like ChatGPT being widely used, concerns have arisen regarding plagiarism and authenticity of student work (Prykhodchenko et al., 2025). 7.4 Resource Inequality – Access to high-quality EAP instruction is uneven globally, disadvantaging students in less-resourced institutions. EAP plays a vital role in supporting students’ academic success by equipping them with the necessary linguistic, cognitive, and critical skills to thrive in higher education. Its needs-based, discipline-sensitive, and literacy-oriented approaches ensure that it remains dynamic and adaptable to global academic trends. However, challenges such as professional recognition, digital integration, and academic integrity require continuous attention. Ultimately, EAP is not merely about mastering English; it is about gaining access to academic communities of practice, shaping professional identities, and engaging critically with knowledge production in a globalised world. References Aarvidurai, E., Anandhan, H., & Thiyagarajan, T.K. (2025). Education as panacea: Paradigm shift in assessing the English for medical purposes needs and learning preferences of … Read more

Business Planning and Strategy: Transforming Opportunities into Profitable Ventures

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An effective business planning and strategy is central to converting promising opportunities into profitable ventures. It serves as a structured document that outlines a company’s vision, mission, objectives, competitive positioning, operations, and financial forecasts (Barringer & Ireland, 2016). However, business planning goes beyond documentation. It is deeply rooted in strategic management, where firms align their internal resources with external market opportunities (Hitt et al., 2008). In today’s fast-paced and digitally transformed environment, methods such as the lean start-up approach (Ries, 2011) and the integration of analytics (Ezeife et al., 2024) play a crucial role in ensuring that plans are adaptable, evidence-based, and profit-oriented. This article explores how business planning and strategy enable organisations to transform opportunities into sustainable profits, supported by theoretical frameworks, practical examples, and current research. 1.0 The Role of Business Planning in Opportunity Transformation A business plan functions as both a roadmap and a communication tool. It clarifies strategic direction, reduces uncertainty, and attracts investors, lenders, and partners (Barringer & Ireland, 2016). According to Borcosi (2025), structured planning is associated with higher levels of employee training, efficiency, and operational profitability. Furthermore, Rezvani et al. (2025) emphasise that planning facilitates entrepreneurial entry by systematically identifying opportunities and mitigating risks. For example, Dropbox successfully validated its idea by releasing a demo video before developing the full product (Ries, 2011). This demonstrates how structured yet flexible planning can reduce upfront costs while gauging market demand. 2.0 Strategic Management as a Profitability Driver Strategic management ensures that firms deploy resources to capture opportunities effectively. Hitt et al. (2008) argue that long-term profitability depends on the alignment of resources, competitive advantage, and environmental scanning. In line with this, Ezeife et al. (2024) propose integrating predictive analytics into strategic decision-making, which boosts profitability and enhances business longevity. Similarly, Jadhav et al. (2025) highlight the emergence of green and sustainable business models, where strategic planning incorporates environmental and social value creation alongside profit objectives. Firms that integrate sustainability into planning, such as Unilever’s Sustainable Living Plan, gain both competitive advantage and customer loyalty. 3.0 The Lean Start-up and Iterative Planning The lean start-up methodology challenges the traditional approach of producing detailed long-term plans upfront. Ries (2011) stresses that businesses should experiment, measure, and learn through minimum viable products (MVPs). This iterative process enables firms to validate assumptions early and pivot when necessary. Khan et al. (2025) demonstrate how firms use machine learning and optimisation models to cut costs and increase profitability in logistics. By combining lean principles with advanced analytics, businesses achieve agility while maximising financial outcomes. 4.0 Digital Transformation and Data-Driven Strategy Digitalisation has fundamentally transformed business planning and execution. According to Vanani et al. (2024), digital transformation technologies—including AI, big data, and advanced analytics—improve decision-making, reduce risks, and enhance profitability. Borcosi (2025) further observes that digital management practices adopted by SMEs allow them to scale operations globally. Human Resource (HR) practices are also key. Ojakorotu (2025) notes that HR analytics informs workforce strategies, aligning talent management with profitability objectives. This data-driven alignment ensures that the human capital dimension supports broader strategic goals. 5.0 Financial Planning and Risk Management Effective planning also requires financial forecasting and risk analysis. According to Mansour and Vadell (2025), green finance and energy transition frameworks demonstrate how integrating financial planning with sustainable strategies reduces risks while ensuring long-term viability. In hospitality, Golja et al. (2024) show how investment models in Croatian hotels emphasise detailed planning to balance customer experience with profitability metrics. Similarly, Shelby (2025) stresses how ownership transitions planned under employee ownership models can sustain profitability while building long-term resilience. 6.0 Case Studies in Strategic Planning Success Several industries provide examples of how planning and strategy transform opportunities: Tourism and Hospitality: Zhang et al. (2025) analyse immigrant entrepreneurs in Tasmania who used detailed market analysis and business planning to create viable ventures in tourism despite initial barriers. Food and Beverage: Burbar et al. (2025) illustrate how Hamburger Restaurants in Palestine leveraged entrepreneurial planning to thrive in challenging environments. Technology Start-ups: Dropbox’s MVP strategy (Ries, 2011) remains a benchmark for cost-effective opportunity validation. Sustainable Enterprises: Jadhav et al. (2025) highlight green models where sustainability integration enhances long-term competitiveness. These examples show that regardless of industry, the core elements of planning, adaptability, and strategic alignment underpin success. 7.0 Challenges in Transforming Opportunities into Profitable Ventures Despite its advantages, business planning faces challenges: Uncertainty in dynamic markets: Predicting customer preferences in rapidly evolving industries like technology remains difficult (Vanani et al., 2024). Overplanning and rigidity: Excessive focus on detailed long-term projections may hinder responsiveness (Ries, 2011). Digital adoption barriers: SMEs often struggle to access resources for advanced digital transformation (Borcosi, 2025). Sustainability trade-offs: Balancing short-term profit with long-term environmental goals is complex (Mansour & Vadell, 2025). Addressing these challenges requires adaptive strategy frameworks, continuous learning, and flexible financing. Business planning and strategy remain indispensable tools for converting opportunities into profitable ventures. They combine vision setting, strategic resource allocation, financial forecasting, and digital adoption with lean experimentation and sustainability considerations. Evidence from multiple industries confirms that structured yet adaptive planning enhances profitability, resilience, and long-term growth. As businesses face increasing uncertainty, adopting data-driven, iterative, and sustainability-focused approaches will be crucial for success in transforming opportunities into lasting profitability. References Barringer, B. R. & Ireland, R. D. (2016). Entrepreneurship: Successfully Launching New Ventures. 5th ed. Harlow: Pearson. Borcosi, B. C. (2025). The trend in management–digital management and how it is implemented by SMEs. Annals – Economy Series, University of Targu Jiu. Available at: https://www.utgjiu.ro/revista/ec/pdf/2025-01/12_Borcosi.pdf. Burbar, M. Y., Jaber, R. Y. & Shkukani, S. J. (2025). Entrepreneurship, start-ups, and business success: Hunger’s Hamburger Restaurant in Palestine. Springer. Ezeife, E., Eyeregba, M. E. & Mokogwu, C. (2024). Integrating predictive analytics into strategic decision-making. World Journal of Management. Available at: https://www.researchgate.net/publication/386336187. Golja, T., Kukurin, Ž. & Prevolšek, D. (2024). Razvojne strategije u hotelijerstvu. CroRIS. Available at: https://www.croris.hr/crosbi/publikacija/resolve/croris/857999. Hitt, M. A., Ireland, R. D. & Hoskisson, R. E. (2008). Strategic Management: Competitiveness and Globalisation. 8th ed. Mason, OH: South-Western. Jadhav, H. L., Pandey, P. P. & … Read more

Entrepreneurship: Opportunity Recognition and Idea Generation

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Opportunity recognition is central to entrepreneurship, as it involves identifying gaps in markets, unmet consumer needs, or emerging trends and translating them into viable ventures (Montiel-Campos, 2023). Without this skill, entrepreneurial activity would lack direction and purpose. The process is often described as entrepreneurial alertness—the ability to scan environments, connect disparate pieces of information, and envision new opportunities (Ardichvili et al., 2003). Famous ventures such as Airbnb and Uber demonstrate the power of opportunity recognition, having emerged from inefficiencies in hospitality and transport sectors. This article examines opportunity recognition and idea generation, highlighting theoretical foundations, influencing factors, and practical examples. 1.0 The Nature of Opportunity Recognition Scholars conceptualise entrepreneurial opportunities as situations where new goods, services, or methods of production can be introduced and sold at greater than their cost (Shane & Venkataraman, 2000). Recognising such opportunities requires not only creativity but also contextual awareness. Baron (2006) argues that entrepreneurs employ pattern recognition, “connecting the dots” between emerging technologies, consumer frustrations, and industry shifts. For example, Spotify recognised the opportunity in the frustration caused by music piracy and declining CD sales, introducing a legal, convenient streaming service. This illustrates that opportunities often arise at the intersection of consumer pain points and technological change. 2.0 Entrepreneurial Alertness A central concept in opportunity recognition is entrepreneurial alertness—the readiness to notice new opportunities without deliberate search (Tang et al., 2012). It involves three dimensions: Scanning and searching for information – keeping track of market trends. Association and connection – combining unrelated information to form new insights. Evaluation and judgment – deciding whether the opportunity is viable. Elon Musk, for example, displayed entrepreneurial alertness by perceiving opportunities in both the electric vehicle and space exploration industries, where traditional players saw insurmountable risks. 3.0 Influencing Factors in Opportunity Recognition Research shows that opportunity recognition is shaped by several factors: 3.1 Prior Knowledge Individuals draw on prior knowledge when recognising opportunities. Shane (2000) found that entrepreneurs with specialised industry knowledge are better positioned to perceive new possibilities. For instance, Jeff Bezos’ experience in finance and e-commerce enabled him to conceptualise Amazon. 3.2 Social Networks Social capital is crucial in opportunity recognition. Aldrich and Zimmer (1986) argue that opportunities are often discovered through networks, where information flows across weak ties. LinkedIn itself emerged as an entrepreneurial opportunity through recognising the value of professional networking online. 3.3 Cognitive Styles Gómez-Gras and Mira-Solves (2010) highlight that entrepreneurs with intuitive and innovative cognitive styles are more likely to identify opportunities than those with purely analytical approaches. Pattern recognition and creative problem-solving play significant roles in bridging market gaps. 3.4 Environmental Dynamism Puhakka (2002) emphasises that opportunities emerge more frequently in dynamic environments. For example, the COVID-19 pandemic accelerated recognition of opportunities in remote work platforms such as Zoom, which saw exponential growth. 4.0 The Role of Idea Generation While recognising opportunities is crucial, idea generation translates recognition into actionable business concepts. Idea generation is both a creative and systematic process involving brainstorming, experimentation, and iteration (Gielnik et al., 2012). These factors below are critical for idea generation: 4.1 Creativity and Innovation Creativity is central to idea generation. Entrepreneurs must generate multiple solutions before selecting viable ones (Hansen et al., 2011). For example, the founders of Airbnb initially experimented with different concepts before settling on peer-to-peer lodging. 4.2 Organisational Learning Lumpkin and Lichtenstein (2005) stress that organisational learning enhances opportunity recognition and idea generation. Firms that foster learning cultures, such as Google, allow employees to explore side projects—leading to products like Gmail. 4.3 Diversity of Information Research shows that diverse information sources foster novel idea generation. Gielnik et al. (2012) found that individuals exposed to multiple industries and knowledge bases generated more innovative business ideas. 5.0 Models of Opportunity Recognition Several models attempt to explain how entrepreneurs identify and develop opportunities: Discovery Theory – Opportunities exist independently and are discovered through search (Shane & Venkataraman, 2000). Creation Theory – Opportunities are constructed through entrepreneur–environment interaction (Alvarez & Barney, 2007). Pattern Recognition Model – Entrepreneurs identify patterns across information and events to form opportunities (Baron, 2006). For example, Tesla aligns with creation theory, as opportunities in electric mobility were not obvious until entrepreneurs like Musk actively shaped consumer and regulatory environments. 6.0 Challenges in Opportunity Recognition Despite its importance, entrepreneurs face barriers in recognising and acting upon opportunities: Cognitive biases: Overconfidence or confirmation bias can lead to pursuing poor ideas (Mitchell et al., 2007). Resource constraints: Recognised opportunities may not be pursued without adequate funding or networks. Market uncertainty: Opportunities that appear attractive may collapse due to unforeseen changes. The case of Segway illustrates these challenges: despite innovative technology, the product failed due to misjudged consumer demand and pricing. 7.0 Practical Approaches to Enhancing Opportunity Recognition 7.1 Entrepreneurial Education Education plays a vital role in developing entrepreneurial alertness. Heinonen et al. (2011) highlight the importance of creativity training and experiential learning in fostering opportunity recognition skills. Business schools now use simulations and incubators to expose students to real-world opportunities. 7.2 Use of Technology Digital tools enhance opportunity recognition by providing access to big data, social media trends, and predictive analytics (George et al., 2016). For example, start-ups use platforms like Google Trends to anticipate consumer interests. 7.3 Collaboration and Networking Engaging in entrepreneurial ecosystems helps entrepreneurs share ideas and gain exposure to opportunities. Silicon Valley remains a prime example where dense networks and knowledge spillovers fuel opportunity recognition and innovation (Spiegler & Halberstadt, 2018). 8.0 Real-World Examples Zoom: Recognised an opportunity in seamless remote communication long before the pandemic but capitalised massively when environmental shifts increased demand. Airbnb: Saw potential in underutilised housing and consumer demand for cheaper, authentic travel experiences. Tesla: Recognised environmental and regulatory trends favouring clean energy, turning electric vehicles into a mass-market product. These examples reinforce that opportunity recognition and idea generation are not isolated skills but interconnected with market dynamics, social networks, and creative cognition. Opportunity recognition and idea generation lie at the core of entrepreneurship. Entrepreneurs rely on alertness, prior knowledge, networks, and creativity to … Read more

The Entrepreneurial Mindset and Characteristics

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The concept of the entrepreneurial mindset has gained significant attention in both academic research and practice, as it encapsulates the cognitive, behavioural, and emotional orientations that enable individuals to identify opportunities, take risks, and create value in uncertain environments. Unlike traditional employees who may seek stability, entrepreneurs thrive in ambiguity and dynamic contexts, often turning volatility into opportunity (Bailetti, 2012). This article explores the entrepreneurial mindset, its core characteristics, and its importance in today’s rapidly evolving business landscape. 1.0 Defining the Entrepreneurial Mindset The entrepreneurial mindset refers to a way of thinking and acting that allows individuals to identify opportunities, innovate, and persevere in the face of challenges. Ratten (2010) describes it as being characterised by creativity, resilience, adaptability, and risk-taking. More recently, Pidduck et al. (2023) conceptualise it as a blend of dispositional beliefs, opportunity beliefs, and entrepreneurial behaviours, suggesting that mindset is not only about traits but also about learned patterns of thought and action. Educational institutions have increasingly recognised the importance of cultivating an entrepreneurial mindset among students, even those not intending to start businesses, because it enhances problem-solving skills, critical thinking, and innovation capacity (Li et al., 2016). For example, the UK has embedded entrepreneurship education into curricula to foster employability and adaptability in graduates (Fayolle & Loi, 2021). 2.0 Key Characteristics of the Entrepreneurial Mindset 2.1 Creativity and Innovation Orientation Entrepreneurs are often described as creative problem-solvers who challenge the status quo and generate novel ideas. Creativity provides the foundation for opportunity recognition, while innovation translates those ideas into viable solutions (Tidd & Bessant, 2020). For instance, Steve Jobs exemplified creativity by integrating design, technology, and user experience, leading to revolutionary products such as the iPhone. Research confirms that creativity and openness to experience are positively linked with entrepreneurial intentions (Al-Ghazali et al., 2022). 2.2 Resilience and Persistence Resilience refers to the ability to recover from setbacks and maintain motivation in the face of adversity. Entrepreneurs frequently experience failure, yet those with a resilient mindset use failure as a learning opportunity (Kuratko et al., 2021). Elon Musk provides a powerful example of resilience, overcoming multiple failures in ventures such as SpaceX before achieving success. According to Ratten (2023), resilience is one of the most critical characteristics in navigating the uncertainties of entrepreneurship. 2.3 Adaptability and Flexibility The entrepreneurial environment is marked by rapid change. Entrepreneurs must continuously adapt their strategies, business models, and products to evolving market needs. Adaptability is seen in companies such as Netflix, which transitioned from DVD rentals to online streaming, later moving into original content production to sustain its competitive advantage. Research highlights that adaptability is associated with opportunity exploitation and firm survival (Naumann, 2017). 2.4 Risk-Taking Propensity A willingness to take calculated risks is a hallmark of entrepreneurship. While entrepreneurs do not engage in reckless behaviour, they are more comfortable with uncertainty and more inclined to pursue high-risk, high-reward opportunities compared to non-entrepreneurs (Mathisen & Arnulf, 2014). For example, Richard Branson consistently pursued ventures in diverse industries, from airlines to space tourism, driven by a risk-tolerant mindset. 2.5 Self-Efficacy Self-efficacy—the belief in one’s capability to perform tasks and achieve goals—is strongly linked to entrepreneurial success (Bandura, 1997; Carlsson et al., 2013). Entrepreneurs with high self-efficacy are more likely to pursue opportunities, persist in adversity, and influence others. Studies show that self-efficacy predicts entrepreneurial intentions across cultures (Kwapisz et al., 2022). 2.6 Proactiveness Entrepreneurs are typically proactive, anticipating future trends and acting before competitors. Proactiveness involves scanning environments for signals and positioning ventures strategically (Daspit et al., 2023). For example, Jeff Bezos anticipated the potential of e-commerce long before it became mainstream, positioning Amazon as a global leader. 2.7 Opportunity Recognition At the core of entrepreneurship lies the ability to identify and evaluate opportunities. Ardichvili et al. (2003) argue that opportunity recognition is influenced by prior knowledge, social networks, and alertness. Entrepreneurs often see connections where others see chaos, turning market gaps into viable ventures. For instance, Airbnb recognised the underutilisation of private homes as a lodging resource, creating an entirely new market. 3.0 Entrepreneurial Mindset in Practice The entrepreneurial mindset is not confined to business founders. Corporate managers, freelancers, and even public sector leaders benefit from entrepreneurial thinking. This has led to the concept of intrapreneurship, where employees apply entrepreneurial characteristics within established organisations to drive innovation (Hisrich & Kearney, 2014). Google’s “20% time” initiative, which allowed employees to pursue personal projects, gave rise to products like Gmail—demonstrating intrapreneurial creativity and autonomy. Moreover, studies show that cultivating an entrepreneurial mindset enhances employability and career adaptability. For example, Ferreira & Gonçalves (2023) found that individuals with entrepreneurial traits such as initiative and resilience are better positioned to thrive in start-ups and fast-paced industries. 4.0 Educational and Policy Implications Given its importance, fostering an entrepreneurial mindset has become a policy and educational priority. Programmes such as the European Commission’s “EntreComp” framework highlight the need for teaching entrepreneurial competences across education levels. Fayolle & Loi (2021) argue that experiential learning, such as business simulations, start-up incubators, and mentoring, is more effective than traditional lectures in developing entrepreneurial characteristics. Governments and institutions can also play a role by creating ecosystems that support entrepreneurial development. Start-up hubs like Silicon Valley thrive not only because of individual entrepreneurs but also due to supportive networks of universities, investors, and policies that encourage risk-taking and innovation (Celestin & Vanitha, 2018). 5.0 Critiques and Limitations of the Entrepreneurial Mindset Concept While the entrepreneurial mindset is widely celebrated, scholars caution against oversimplification. Some argue that mindset is context-dependent and cannot be reduced to a fixed set of traits (Belousova & Hattenberg, 2021). Others highlight the potential downsides: excessive risk-taking may lead to financial instability, while relentless proactiveness can cause burnout (Kuratko et al., 2021). Additionally, cultural differences influence how entrepreneurial traits are expressed. For example, risk-taking may be more valued in Western contexts but less accepted in collectivist cultures where failure carries higher social costs (Srivastava, 2025). Therefore, a nuanced understanding of entrepreneurial mindset must consider individual, cultural, and … Read more

Role of Management Information Systems (MIS) at the Operational Level: Enhancing Efficiency and Accuracy

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Management Information Systems (MIS) are essential for supporting organisational decision-making across all levels of management. At the operational level, MIS plays a pivotal role in automating routine processes, reducing human error, and improving efficiency in areas such as payroll, inventory management, and sales reporting (Zorina & Zorin, 2025). By streamlining these activities, organisations free employees to focus on higher-value tasks, ultimately improving productivity. This article examines operational-level MIS, focusing on its applications in payroll, sales reporting, and inventory management. It also explores Tesco’s implementation of MIS and evaluates the challenges and future directions of operational-level systems. Understanding Operational-Level MIS At the operational level, MIS is concerned with structured, repetitive tasks that underpin day-to-day business activities. These tasks typically involve transaction processing systems (TPS), which record and manage transactions such as payroll entries, point-of-sale (POS) data, and inventory updates (Laudon & Laudon, 2022). The benefits of operational-level MIS include: Accuracy – automation reduces manual input errors. Efficiency – tasks such as payroll calculation are completed more quickly. Cost Savings – less reliance on manual labour. Timeliness – managers receive near real-time updates on key operational metrics. Payroll Systems One of the most common applications of MIS at the operational level is in payroll management. Payroll systems automate salary calculations, tax deductions, and benefits management, ensuring compliance with employment regulations and consistency in employee compensation (Whiteley, 2017). For example, large retailers such as Tesco employ payroll systems that integrate with timekeeping and attendance records, ensuring accurate wage calculations for thousands of employees (Meng, 2024). This eliminates errors associated with manual data entry and reduces disputes between staff and employers. Sales Reporting Sales reporting systems form another critical application of operational-level MIS. Through POS systems, retailers automatically record every transaction, which is then aggregated into daily, weekly, and monthly sales reports (Saani, 2019). These reports enable managers to identify top-selling products, measure employee performance, and detect anomalies such as theft or errors. In practice, Tesco has integrated sales reporting into its ERP and MIS platforms, providing real-time visibility into store-level and company-wide performance (Kukreja & Gupta, 2016). This allows Tesco to detect patterns such as regional variations in demand, enabling more precise marketing campaigns. Inventory Management Perhaps the most significant contribution of operational-level MIS lies in inventory management. Inventory systems automate stock tracking, replenishment, and forecasting to avoid overstocking or stockouts. Advanced systems employ techniques such as Just-in-Time (JIT) and Economic Order Quantity (EOQ) to optimise stock levels (Meng, 2024). Tesco is a prime example of MIS in inventory management. The company uses automated systems to track product sales in real time, triggering reordering processes to avoid shortages (Potter & Disney, 2010). This system is supported by data-driven supply chain models, helping Tesco mitigate the bullwhip effect and maintain customer satisfaction. Tesco: A Case Study in Operational-Level MIS Tesco’s integration of MIS across its operations demonstrates how automation enhances efficiency: Payroll – Tesco’s payroll system integrates employee attendance data to automate wage processing, ensuring compliance with wage laws (Kukreja & Gupta, 2016). Sales Reporting – POS systems at Tesco stores feed into a central MIS, generating daily sales summaries that inform short-term marketing and staffing decisions (Farooq, 2021). Inventory Management – Tesco leverages advanced replenishment systems, reducing stockouts and waste by ensuring products are restocked based on real-time demand (Potter & Disney, 2010). By combining these systems, Tesco has achieved operational resilience, improved accuracy, and reduced costs, positioning itself as a leader in retail efficiency. Challenges in Operational-Level MIS Despite its benefits, operational-level MIS faces several challenges: Data Quality – inaccurate input can still compromise the system’s output (Ring & Tigert, 2001). Integration Issues – linking payroll, sales, and inventory systems across multiple locations is complex. Cybersecurity Risks – sensitive payroll and sales data must be protected against breaches (Woods, 2022). Cost – the initial investment in MIS infrastructure can be significant, particularly for small businesses (Mukhopadhyay, 2009). User Resistance – staff may resist system adoption if adequate training is not provided. For Tesco, managing such challenges involves continuous investment in IT infrastructure, employee training, and robust data governance strategies (Rahman et al., 2025). Future Directions of Operational-Level MIS The future of operational MIS will be shaped by: Artificial Intelligence (AI) – enabling predictive payroll analytics (e.g., turnover forecasting) and inventory forecasting (Stone & Hollier, 2000). Cloud Computing – offering scalable and cost-effective MIS solutions for global retailers (Luftman & Kempaiah, 2008).  IoT Integration – using smart shelves and sensors to update inventory systems in real time (Richards, 2017).  Blockchain – providing secure payroll processing and transparent supply chains (Whiteley, 2017). Retailers like Tesco are already exploring these innovations, integrating AI-driven demand forecasting and IoT-based stock management into their MIS to further enhance efficiency and reduce costs. Operational-level MIS plays a vital role in automating routine processes such as payroll, sales reporting, and inventory management, enabling organisations to reduce errors, improve efficiency, and focus on higher-value tasks. Tesco serves as a leading case study, demonstrating how MIS can transform retail operations through automation and data-driven decision-making. While challenges remain, the integration of emerging technologies such as AI, IoT, and blockchain promises to further advance operational-level MIS. For businesses operating in highly competitive environments, mastering these systems is no longer optional but a necessity for survival and growth. References Farooq, M.U. & Comanoiu, A.M.B. (2021) Strategic Synergy: Leveraging Big Data and Information Systems for Competitive Advantage in the Digital Age. York St John University. [Available at: https://ray.yorksj.ac.uk/id/eprint/11156/]. Kukreja, G. & Gupta, S. (2016) Tesco Accounting Misstatements: Myopic Ideologies Overshadowing Larger Organisational Interests. SDMIMD Journal of Management. [Available at: https://www.researchgate.net/publication/309519208]. Laudon, K.C. & Laudon, J.P. (2022) Management Information Systems: Managing the Digital Firm. 17th ed. Pearson: Harlow. Luftman, J. & Kempaiah, R. (2008) Key Issues for IT Executives 2007. MIS Quarterly Executive, 7(2), pp. 91–99. Meng, S. (2024) Operation Management of Grocery Retailer in the UK: A Case of Tesco. Pacific International Journal. [Available at: https://rclss.com/pij/article/view/586]. Mukhopadhyay, J. (2009) Supply Chain Management: A Comparative Study Between Large Organised Food and Grocery Retailers in India. … Read more

Business Analytics: An Overview of Key Concepts and Applications

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In today’s data-driven economy, organisations are increasingly reliant on Business Analytics (BA) to make informed decisions, enhance efficiency, and gain competitive advantage. BA can be defined as the systematic exploration of data through statistical and computational methods to generate insights for decision-making (Evans, 2016). It is generally categorised into three types: descriptive analytics, predictive analytics, and prescriptive analytics, each serving a distinct but complementary role. This article explores the foundations of BA, its categories, tools, and techniques, as well as its applications across industries such as healthcare, retail, and aviation. It also considers the challenges of implementation and outlines future directions shaped by big data, machine learning (ML), and artificial intelligence (AI). The Core Types of Business Analytics Descriptive Analytics Descriptive analytics summarises past data to reveal patterns and trends. It involves reporting, visualisation, and statistical summaries, helping organisations understand what has already happened (Evans, 2016). For instance, a retailer may use sales dashboards to identify the best-performing product lines or seasonal demand fluctuations (Iyer & Iyer, 2025). Predictive Analytics Predictive analytics employs statistical modelling, regression analysis, and machine learning to forecast future outcomes (Vyas & Patel, 2025). Airlines such as British Airways use predictive analytics to forecast customer demand, optimise ticket pricing, and manage route profitability (Habib et al., 2025). Prescriptive Analytics Prescriptive analytics goes beyond forecasting by recommending optimal actions. It combines predictive models with optimisation algorithms to suggest strategies for maximising performance. For example, logistics firms use prescriptive analytics to determine the most efficient delivery routes, balancing costs and customer service (Joshua, 2025). Tools and Techniques in Business Analytics BA leverages a range of tools and methods, including: Data Mining – uncovering hidden patterns in large datasets. Statistical Modelling – using regression, correlation, and hypothesis testing for structured analysis (Wiranegara, 2025). Machine Learning (ML) – applying algorithms such as decision trees, clustering, and neural networks for classification and prediction (Cheriyan, 2025). Optimisation – solving mathematical models to determine the most efficient allocation of resources (Modalavalasa & Kali, 2025). Visualisation Tools – dashboards and BI platforms such as Tableau and Power BI, which present data in accessible forms (Jain, 2025). For instance, Microsoft uses Power BI dashboards to integrate sales, marketing, and customer service data, enhancing real-time decision-making (Jain, 2025). Applications Across Industries Retail and E-commerce Retailers like Tesco employ BA to track consumer purchasing patterns, manage supply chains, and personalise promotions (Rahman et al., 2025). Amazon applies predictive analytics to suggest products, improving cross-selling and upselling. Healthcare In healthcare, BA supports predictive modelling for disease outbreaks and patient risk analysis. Hospitals use prescriptive analytics to optimise resource allocation, such as ICU bed management during peak demand (Wu et al., 2025). Finance and Banking Financial institutions utilise BA for fraud detection, credit scoring, and risk management. Predictive models help banks anticipate loan defaults, while prescriptive models assist in portfolio optimisation (Thiyagarajan, 2025). Aviation Airlines like British Airways and Lufthansa use BA to optimise fuel consumption, flight schedules, and ticket pricing, significantly improving profitability and customer satisfaction (Habib et al., 2025). Marketing BA enables customer segmentation, campaign analysis, and sentiment analysis. Firms can track social media trends and customer feedback to shape marketing strategies (Ahmed, 2025). The Role of Big Data and AI in Business Analytics The rise of big data has revolutionised BA by enabling organisations to analyse massive, unstructured datasets from IoT devices, social media, and digital transactions (Modalavalasa & Kali, 2025). Artificial Intelligence (AI) enhances predictive and prescriptive analytics by enabling self-learning models capable of improving accuracy over time (Sabapathy, 2025). For example, Netflix uses AI-driven analytics to recommend content to users, driving engagement and retention. Similarly, in manufacturing, predictive maintenance powered by BA minimises downtime by forecasting equipment failures. Challenges in Business Analytics Despite its transformative potential, BA faces key challenges: Data Quality – poor data integrity can undermine analytical accuracy (Pham, 2025). Integration Issues – combining structured and unstructured data from multiple sources is complex. Privacy Concerns – stricter regulations such as GDPR necessitate ethical and compliant use of customer data (Khalique, 2025). Skills Gap – organisations face shortages of professionals proficient in both analytics tools and business strategy (Cheriyan, 2025). Cost and Complexity – BA infrastructure requires significant investment in technology and skilled staff (Drvarek, 2025). Future Directions The future of BA is shaped by emerging technologies and methodologies: AI-Powered Analytics – deeper integration of machine learning for autonomous decision-making (Iyer & Iyer, 2025). Real-Time Analytics – increasing demand for streaming analytics in sectors like finance and e-commerce (Wu et al., 2025). Explainable AI (XAI) – ensuring that advanced predictive models remain transparent and interpretable (Sabapathy, 2025). Cloud-Based Analytics – scalable, flexible solutions accessible to firms of all sizes (Mamun, 2025). Industry-Specific Analytics – tailored solutions for healthcare, retail, and manufacturing. For example, smart cities are applying BA integrated with IoT to improve traffic management, energy consumption, and environmental monitoring (Sudha et al., 2026). Business Analytics is a critical enabler of informed decision-making, innovation, and sustainable growth in modern organisations. By integrating descriptive, predictive, and prescriptive analytics, firms can harness data to understand historical performance, anticipate future challenges, and take proactive measures. From retail to healthcare and aviation, BA has transformed operational efficiency and customer experience. While challenges such as data quality, privacy, and skills gaps remain, the integration of AI, big data, and cloud computing promises to make BA more powerful, accessible, and impactful. For students and practitioners alike, understanding BA is essential for navigating the digital economy and leveraging data as a strategic asset. References Ahmed, S. (2025) Evolution and Importance of Business Analytics in Modern Business. ResearchGate. [Available at: https://www.researchgate.net/publication/379975634]. Cheriyan, V. (2025) Navigating the Analytics Landscape: A Comprehensive Guide for Recent Graduates. Journal of Computer Science and Technology. [Available at: https://al-kindipublishers.org/index.php/jcsts/article/view/9845]. Drvarek, S. (2025) Konceptualni model poslovne analitike korištenjem podatkovne analitike. Repozitorij Unin. [Available at: https://repozitorij.unin.hr/en/islandora/object/unin:7720]. Evans, J.R. (2016) Business Analytics: Methods, Models, and Decisions. 2nd ed. Pearson, Harlow. Habib, M.A., Mahmood, A., Ahmad, M. & Baig, S.A. (2025) Barriers to Adoption Industry 4.0 in Textile Sector. IEEE … Read more

Management Information Systems: A Critical Overview

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Management Information Systems (MIS) form a cornerstone of modern business, enabling organisations to streamline operations, improve decision-making, and gain competitive advantage. MIS can be defined as computer-based systems that provide managers with structured, timely, and relevant information to support planning, control, and decision-making (O’Brien & Marakas, 2019). This article critically explores the role of MIS within organisations, its functional levels (operational, tactical, and strategic), integration with other systems, and its transformation in the era of big data, cloud computing, and artificial intelligence (AI). Real-world examples from industries such as retail, logistics, and finance are provided to illustrate practical applications. The Role and Definition of MIS MIS is distinguished from general information systems by its explicit focus on management support. While basic information systems handle data collection and processing, MIS transforms this data into actionable insights for managers (Laudon & Laudon, 2022). The primary functions of MIS include: Data Collection – gathering raw data from internal and external sources. Data Processing – converting raw data into meaningful information through classification, summarisation, and analysis. Information Distribution – delivering the right information to the right people at the right time. For example, in the banking sector, MIS facilitates real-time transaction monitoring, ensuring that branch managers can monitor liquidity, customer service, and compliance simultaneously (Liu et al., 2025). Levels of MIS Support The role of MIS can be divided into three distinct levels: 1.0 Operational Support At the operational level, MIS automates routine processes such as payroll, sales reporting, and inventory management. This reduces manual errors, improves efficiency, and enables staff to focus on higher-value activities (Zorina & Zorin, 2025). For instance, supermarkets like Tesco use MIS to automatically track product sales and reorder stock to avoid shortages (Rahman et al., 2025). 2.0 Tactical Decision Support At the tactical level, MIS provides mid-level managers with reports and dashboards to monitor performance and allocate resources effectively. For example, logistics companies employ MIS to track fleet movements, identify bottlenecks, and optimise delivery schedules (Lekic & Lekic, 2025). Strategic Planning At the strategic level, MIS helps senior executives analyse long-term trends, align IT with business objectives, and assess potential risks. For instance, multinational corporations use MIS to assess global market trends, enabling them to plan expansions and manage financial risks more effectively (Yang, 2025). Integration with Other Systems MIS does not exist in isolation. It is often integrated with: Decision Support Systems (DSS) – which provide analytical tools for complex, unstructured decisions. Knowledge Management Systems (KMS) – which facilitate knowledge sharing and organisational learning. Enterprise Resource Planning (ERP) systems – which unify business processes across departments. For example, in manufacturing, ERP systems provide operational data, MIS analyses this data, and DSS supports decisions such as whether to expand production capacity (Manurung et al., 2025). This integrated approach ensures a holistic view of the organisation. MIS and Business Intelligence The emergence of Business Intelligence (BI) has transformed traditional MIS. BI tools allow organisations to move beyond simple reporting towards predictive and prescriptive analytics. According to Pettersson and Rooth (2025), organisations that adopt BI-enhanced MIS achieve higher levels of decision accuracy and financial performance. For example, airlines use MIS integrated with BI to predict seasonal demand, optimise ticket pricing, and allocate resources to profitable routes (Butaboev et al., 2025). MIS in Different Sectors Retail Retailers like Tesco and Amazon use MIS to track consumer preferences, optimise inventory, and personalise marketing campaigns (Rahman et al., 2025). Healthcare MIS supports Electronic Health Records (EHRs), enabling hospitals to store patient data, track treatment outcomes, and support evidence-based clinical decisions (Saremi, 2025). Finance Banks deploy MIS for risk management, fraud detection, and compliance reporting. By integrating with AI, MIS enables real-time alerts on suspicious transactions (Guericke et al., 2025). Challenges in MIS Implementation Despite its benefits, MIS faces several challenges: Data Quality Issues – inaccurate or incomplete data can undermine decision-making (Zorina & Zorin, 2025). Cybersecurity Risks – as systems store sensitive information, they are vulnerable to cyberattacks (Yang, 2025). User Resistance – employees may resist adoption due to lack of training or fear of monitoring. Cost and Complexity – large-scale MIS projects often require significant investment and change management. For example, retail firms implementing MIS often struggle with data integration from multiple stores and online platforms, highlighting the need for robust data governance frameworks (Uboh, 2025). The Future of MIS The future of MIS is shaped by emerging technologies: Artificial Intelligence and Machine Learning – enabling predictive analytics and automated decision-making (Raja, 2025). Cloud Computing – allowing scalable, cost-efficient MIS accessible globally (Yang, 2025). Big Data Analytics – enhancing the ability to process vast, unstructured datasets for strategic insights (Liu et al., 2025). Blockchain – improving security, transparency, and accountability in MIS processes (Guericke et al., 2025). For example, smart cities are deploying MIS integrated with IoT to manage traffic flows, energy usage, and public safety, demonstrating MIS’s growing societal role (Melnykova, 2025). Management Information Systems (MIS) are indispensable in modern business, enabling operational efficiency, tactical performance monitoring, and strategic planning. By integrating with DSS, KMS, and BI, MIS provides comprehensive support for decision-making. While challenges such as data quality, cybersecurity, and cost remain, emerging technologies such as AI and cloud computing are redefining the potential of MIS. As businesses continue to embrace digital transformation, MIS will evolve from being a support tool into a strategic enabler of innovation and competitiveness. For students and practitioners, mastering MIS is therefore essential for navigating the complexities of the digital economy. References Butaboev, M., Qodirov, B. & Askarov, X. (2025) Big Data as the oil of the digital economy. BIO Web of Conferences. [Available at: https://www.bio-conferences.org/articles/bioconf/pdf/2025/37/bioconf_isotobat2025_02009.pdf]. Guericke, D., Trivella, A. & Nielsen, T.L. (2025) A Framework for Energy Management Modelling in Hubs for Circularity. arXiv preprint. [Available at: https://arxiv.org/abs/2508.19765]. Laudon, K.C. & Laudon, J.P. (2022) Management Information Systems: Managing the Digital Firm. 17th ed. Pearson, Harlow. Lekic, S. & Lekic, N. (2025) Family Businesses and Green Transformation: The Role of Logistics and Artificial Intelligence. manub.org.rs. [Available at: https://manub.org.rs/wp-content/uploads/2025/08/Snezana-Lekic.pdf]. Liu, H.W., Lee, H. & Chang, … Read more

Information Systems and Technology: Overview of Key Study Topics Within the Field

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The field of Information Systems and Technology (IS/IT) plays a crucial role in shaping how modern organisations operate, compete, and innovate. As businesses increasingly depend on data-driven decision-making, IS/IT provides the infrastructure and analytical tools required to process information efficiently. Within this broad discipline, two key study areas stand out: Management Information Systems (MIS) and Business Analytics (BA). These domains not only influence organisational efficiency but also underpin strategic decision-making in an era of rapid technological change. This article provides an overview of these major study topics, drawing on academic literature, textbooks, and professional practice to explain their relevance and applications. Information Systems and Technology Information Systems (IS) refer to organised combinations of people, processes, hardware, software, and networks that collect, process, store, and distribute information to support decision-making and control within organisations (Laudon & Laudon, 2022). In contrast, Information Technology (IT) focuses more narrowly on the hardware and software components that enable such systems. Together, IS/IT forms the backbone of organisational infrastructure, supporting everything from day-to-day operations to high-level strategic initiatives. IS/IT covers diverse areas such as databases, networking, cybersecurity, decision support systems, and enterprise applications. For instance, Enterprise Resource Planning (ERP) systems integrate various business processes (such as finance, HR, and supply chain) into a unified platform, enabling efficiency and transparency (Stair & Reynolds, 2021). The study of IS/IT therefore bridges both technical and managerial perspectives, making it essential for students and professionals in business and technology disciplines alike. Two distinct fields of Information Systems and Technology are: 1.0 Management Information Systems Management Information Systems (MIS) focus specifically on the use of IT to support business operations, management, and decision-making. MIS can be described as computer-based systems that provide managers with the information needed to plan, control, and make informed decisions (O’Brien & Marakas, 2019). The role of MIS can be broken into three levels: Operational Support – automating routine processes such as payroll, inventory tracking, and sales reporting. Tactical Decision Support – providing mid-level managers with structured information to monitor performance. Strategic Planning – enabling senior executives to analyse long-term trends and align IT systems with business objectives. For example, a retailer such as Tesco uses MIS to track consumer purchasing patterns, optimise stock levels, and enhance supply chain coordination (Rahman et al., 2025). MIS is often integrated with other Decision Support Systems (DSS) and Knowledge Management Systems (KMS). With the rise of cloud computing and artificial intelligence (AI), MIS are evolving to deliver more real-time, predictive insights (Saremi, 2025). 2.0 Business Analytics Business Analytics (BA) represents another critical area of IS/IT, focusing on the systematic exploration of data to generate insights for decision-making. BA is often categorised into three types (Evans, 2016): Descriptive Analytics – summarising past data to understand historical patterns. Predictive Analytics – using statistical and machine learning models to forecast future outcomes. Prescriptive Analytics – recommending optimal actions based on predictive insights. Techniques within BA include data mining, machine learning, statistical modelling, and optimisation. For instance, airlines such as British Airways employ predictive analytics to forecast demand, adjust ticket pricing, and optimise flight schedules (Habib et al., 2025). The rise of big data has expanded the scope of BA, enabling organisations to process massive datasets from sources such as social media, IoT sensors, and customer transactions. However, effective BA requires not only technical expertise but also business acumen, as insights must be translated into practical strategies (Pettersson & Rooth, 2025). Integrating MIS and Business Analytics While MIS and BA are distinct study areas, they are increasingly interconnected. MIS provides the infrastructure and databases that capture and store information, whereas BA extracts insights and predictive patterns from this data. Together, they enable evidence-based decision-making. For example, in supply chain management, MIS systems track inventory levels and logistics, while BA models optimise delivery routes and forecast demand variability (John et al., 2025). Similarly, in healthcare, MIS stores patient records, and BA helps identify risk factors for diseases, improving clinical decision-making (Shojaei et al., 2025). This integration underscores the importance of cross-disciplinary learning, where students of IS/IT are encouraged to develop both technical and analytical competencies. Challenges and Future Directions Despite their benefits, MIS and BA face several challenges: Data Quality and Integration – Organisations often struggle with fragmented data sources and inconsistent formats. Privacy and Security – With stricter regulations such as GDPR, ensuring ethical use of data is critical (Hang et al., 2025). Skills Gap – There is a growing demand for professionals who can bridge the gap between technical expertise and business insight. Emerging technologies such as artificial intelligence, blockchain, and quantum computing are likely to redefine the field further. Scholars argue that future MIS will need to be more adaptive, decentralised, and intelligent, while BA will increasingly leverage real-time analytics and explainable AI (Diaz et al., 2025). The study of Information Systems and Technology encompasses multiple dimensions, but Management Information Systems and Business Analytics are two of the most critical areas shaping organisational success today. MIS ensures that organisations can capture and manage data effectively, while BA transforms this data into actionable insights. Together, they contribute to efficiency, innovation, and competitive advantage. As organisations confront the challenges of big data, cybersecurity, and digital transformation, the demand for expertise in MIS and BA will only intensify. For students and professionals, mastering these domains not only provides practical skills but also fosters a deeper understanding of how technology drives business value. References Diaz, R., Tariq, S., Luna, E., Bruin, X. & Martrat, J. (2025) A survey on 5G private and B5G network threats and safeguarding AI-based security mechanisms through the layered analysis. Computer Networks. [Available at: https://www.sciencedirect.com/science/article/pii/S1389128625005614]. Evans, J.R. (2016) Business Analytics: Methods, Models, and Decisions. 2nd ed. Pearson: Harlow. Habib, M.A., Mahmood, A., Ahmad, M. & Baig, S.A. (2025) Barriers to Adoption Industry 4.0 in Textile Sector: Analysing Challenges in Transitioning to Smart Manufacturing with ISM and MICMAC. IEEE Access. [Available at: https://ieeexplore.ieee.org/document/11129254]. Hang, F., Xie, L., Zhang, Z., Guo, W. & Li, H. (2025) Overview of Privacy Protection Based on Joint … Read more

Procurement and Sourcing: Strategic Supplier Management for Competitive Advantage

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Procurement and sourcing play a vital role in contemporary business operations by ensuring a steady flow of raw materials, services, and components required for production. Effective procurement practices involve supplier selection, contract negotiation, and relationship management (Monczka et al., 2020). In today’s globalised and competitive environment, firms are under pressure to optimise costs, guarantee quality, and foster innovation through strategic sourcing and supplier relationship management (SRM). This article explores the critical dimensions of procurement and sourcing, supported by examples from manufacturing, construction, healthcare, and service industries. Theoretical Foundations of Procurement and Sourcing Procurement refers to the acquisition of goods and services from external sources, while sourcing involves identifying, evaluating, and engaging suppliers to deliver these goods and services (Shivdas et al., 2025). Strategic sourcing integrates procurement decisions with organisational goals, making it a core driver of competitive advantage. According to Monczka et al. (2020), organisations that treat procurement as a strategic function—rather than a transactional one—benefit from reduced costs, improved supplier performance, and enhanced innovation. Textbooks in supply chain management emphasise that total cost of ownership (TCO), risk management, and sustainability should guide sourcing decisions (Chopra & Meindl, 2021). Supplier Selection and Evaluation One of the most critical aspects of procurement is supplier selection, which determines the organisation’s ability to achieve cost efficiency and product quality. Traditional criteria such as price, quality, and delivery reliability are now supplemented by sustainability, innovation capability, and risk resilience (Forozandeh & Khalafi, 2025). For example, in the healthcare sector, Shivdas et al. (2025) propose a novel framework for supplier classification, highlighting how selecting the right supplier reduces supply disruptions and improves patient outcomes. Similarly, Seikku (2025) shows how Finnish forest industry firms incorporate environmental, social, and governance (ESG) criteria into supplier selection, ensuring alignment with sustainability goals. The use of multi-criteria decision-making methods such as the Analytic Hierarchy Process (AHP) and TOPSIS has been widely applied to evaluate suppliers, balancing trade-offs between cost, quality, and risk (Forozandeh & Khalafi, 2025). Contract Negotiation and Management Negotiating contracts is essential for establishing terms that balance risk and value for both parties. Albuschus Svanvik and Chouri (2025) show that Swedish private sector firms increasingly rely on fact-based negotiations, leveraging data analytics to achieve fair pricing and performance guarantees. Contracts also need to include service-level agreements (SLAs), risk-sharing clauses, and compliance with sustainability standards. For example, multinational companies sourcing from low-cost countries are now incorporating social responsibility clauses to address concerns over labour exploitation (Pintuma et al., 2025). Strategic Supplier Relationships Beyond transactional exchanges, modern procurement emphasises long-term partnerships through Supplier Relationship Management (SRM). Strategic supplier collaboration enables co-innovation, risk reduction, and better cost control (Bargouthi et al., 2025). In the construction industry, Ako and Peculiar (2025) found that strategic procurement practices improve project performance by fostering stronger vendor relationships. Similarly, in the hospitality sector, Sharma (2025) highlights how B2B procurement practices focus on building trust and collaboration to improve service delivery. An illustrative example is Toyota’s keiretsu model, where close ties with suppliers enable continuous improvement and innovation, demonstrating the importance of supplier integration into long-term business strategies. Technology and E-Procurement Technological advancements such as e-procurement platforms, cloud systems, and artificial intelligence (AI) are transforming procurement. Tatini (2025) argues that AI-driven procurement agents can analyse market conditions, supplier performance, and risk factors to support decision-making. Perante et al. (2025) highlight how cloud-based procurement systems improve efficiency by automating purchase orders, inspection, and reporting. Similarly, Vencentius and Semente (2025) stress that SMEs adopting e-procurement face challenges such as high implementation costs, but gain benefits in transparency and cost efficiency. Sustainability and Ethical Procurement Modern sourcing is increasingly shaped by sustainability and ethical considerations. Organisations are expected to ensure that their suppliers adhere to environmental and labour standards. Omondi et al. (2025) show that NGOs in Kenya emphasise green procurement to reduce environmental impact. For example, multinational companies like Unilever enforce strict sustainability requirements across their supplier base, ensuring compliance with ethical sourcing standards. Seikku (2025) demonstrates how ESG criteria guide supplier choices in the forest industry, aligning procurement with corporate social responsibility (CSR) goals. Global Procurement Challenges Despite its advantages, procurement faces challenges including supply chain disruptions, price volatility, and geopolitical risks. Pintuma et al. (2025) discuss the strategic risks of low-cost country sourcing, where firms often face trade-offs between cost savings and risk exposure. The COVID-19 pandemic revealed vulnerabilities in global supply chains, emphasising the need for resilient procurement strategies (Zapinski, 2025). Companies are now prioritising supplier diversification and regional sourcing to mitigate future disruptions. Case Studies and Industry Applications Healthcare – Supplier classification models reduce costs and improve patient outcomes (Shivdas et al., 2025). Construction – Strategic procurement improves project delivery timelines and quality (Ako & Peculiar, 2025). Hospitality – B2B procurement fosters strong supplier partnerships for service excellence (Sharma, 2025). Manufacturing – Toyota’s keiretsu demonstrates the benefits of long-term supplier integration. Forestry Industry – Finnish firms use ESG metrics to balance sustainability with operational efficiency (Seikku, 2025). Procurement and sourcing are no longer administrative functions but strategic enablers of competitive advantage. By effectively managing supplier selection, negotiation, and relationship development, firms can reduce costs, enhance quality, and promote innovation. Moreover, technology, sustainability, and resilience are shaping the future of procurement, demanding greater agility and long-term partnerships. Companies that view procurement strategically, rather than transactionally, will gain significant competitive advantages in an uncertain global marketplace. References Ako, E. & Peculiar, N.N. (2025). Procurement management practices as a strategic tool for project performance: The case of construction companies in the Northwest Region, Cameroon. International Journal of Business Research and Management. [Available at: https://isapublisher.com/wp-content/uploads/2025/01/Procurement-Management-Practices-as-a-Strategic-Tool-for-Project-Performance-The-Case-of-Construction-Companies-in-the-Northwest-Region-Cameroon-ISAP.pdf]. Albuschus Svanvik, H. & Chouri, H. (2025). To what extent are fact-based price negotiations with existing suppliers used in the Swedish private sector? Chalmers University of Technology. [Available at: https://odr.chalmers.se/items/197f439d-26d0-4b2a-baf2-a46afa541103]. Bargouthi, M.I., Bharadwaj, P. & Byramjee, F. (2025). The influence of selected supply chain management practices on SMEs performance: The mediating role of competitive advantage. Journal of Global Operations and Strategic Sourcing. [DOI: 10.1108/jgoss-08-2024-0071] Chopra, S. & Meindl, P. (2021). Supply Chain Management: … Read more

Supply Chain Management: Concepts, Practices, and Contemporary Developments

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Supply Chain Management (SCM) refers to the coordination, integration, and optimisation of activities involved in sourcing, procurement, production, logistics, and distribution, ensuring that goods and services flow smoothly from suppliers to end consumers (Christopher, 2016). The ultimate aim of SCM is to enhance customer value, improve efficiency, and build sustainable competitive advantage (Chopra & Meindl, 2019). With increasing globalisation, the rapid advancement of digital technologies, and the rise of consumer expectations, SCM has evolved into a critical function that determines organisational competitiveness. For instance, Amazon’s success is attributed largely to its sophisticated logistics and distribution networks, which enable faster delivery and higher levels of customer satisfaction compared to traditional retailers (Tan, 2001). 1.0 Evolution of Supply Chain Management Historically, SCM was viewed as an extension of purchasing and logistics functions. In the 1990s, the term “supply chain management” gained prominence as organisations began adopting a more integrated approach that included suppliers, manufacturers, distributors, and retailers in a single strategic framework (Handfield et al., 2009). According to Tan (2001), SCM shifted the focus from cost reduction alone to value creation, emphasising responsiveness, flexibility, and long-term partnerships. This transition marked the rise of strategic sourcing, global procurement, and just-in-time (JIT) production systems, all of which remain central in modern SCM practice. 2.0 Core Components of SCM SCM comprises several interconnected functions that together ensure effective operations across the value chain. 2.1 Sourcing and Procurement Sourcing involves identifying, evaluating, and selecting suppliers, while procurement relates to acquiring the necessary materials and services. Effective procurement ensures cost efficiency, quality consistency, and supply reliability (Lysons & Farrington, 2006). For example, Toyota’s lean supply chain relies on long-term supplier relationships, which reduce transaction costs and ensure reliable delivery schedules (Schiele, 2018). Strategic sourcing techniques, such as dual sourcing and e-procurement systems, have emerged as powerful tools to reduce risks and improve agility (Amorim & Almada-Lobo, 2014). 2.2 Production Production planning coordinates the transformation of raw materials into finished goods. Effective production relies on demand forecasting, capacity planning, and inventory control (Vidal & Goetschalckx, 1997). Companies such as Dell pioneered build-to-order models, reducing inventory holding costs while increasing responsiveness to customer preferences (Erengüç, Simpson & Vakharia, 1999). 2.3 Distribution and Logistics Distribution management ensures that products reach customers at the right time and in the right condition. Logistics functions, including transportation, warehousing, and last-mile delivery, play a vital role in customer satisfaction (Park, 2005). Amazon’s Prime delivery service illustrates how distribution efficiency can differentiate a company in competitive markets. 3.0 Globalisation and Digital Transformation The increasing globalisation of trade has extended supply chains across continents, making them more complex and vulnerable to risks. Companies now manage global sourcing networks, balancing cost advantages with challenges such as currency fluctuations, political instability, and sustainability pressures (Zeng, 2003). Furthermore, digital technologies such as Artificial Intelligence (AI), blockchain, and the Internet of Things (IoT) have revolutionised SCM. For example, blockchain ensures supply chain transparency by providing immutable records of transactions, while IoT-enabled sensors improve real-time tracking of shipments (Di Pasquale, Nenni & Riemma, 2020). 4.0 Sustainability in Supply Chain Management Sustainability has emerged as a strategic priority in SCM. Companies face growing pressure from governments, consumers, and stakeholders to adopt environmentally responsible and ethically sound practices (Andersen & Rask, 2003). Green supply chain management (GSCM) integrates environmental considerations into procurement, production, and distribution decisions. For instance, Unilever has committed to reducing carbon emissions across its supply chain by sourcing renewable energy and minimising waste (Golini & Kalchschmidt, 2011). 5.0 Challenges in Supply Chain Management Despite advancements, SCM faces several pressing challenges: Supply chain disruptions: Events such as the COVID-19 pandemic exposed vulnerabilities in global supply chains, causing widespread shortages (Gundlach, Bolumole & Eltantawy, 2006). Rising consumer expectations: Customers demand faster delivery and greater personalisation, forcing firms to innovate (Giunipero & Brand, 1996). Balancing cost and resilience: Organisations must strike a balance between efficiency (lean operations) and resilience (risk mitigation strategies) (Zeng, 2003). Integration of digital tools: While digitalisation offers opportunities, it requires significant investment and change management (Ross, 2015). 6.0 Case Examples of Effective SCM Zara – The fashion retailer has developed an agile supply chain, enabling it to move designs from concept to store shelves in as little as two weeks. This rapid responsiveness provides a competitive edge in the fast fashion industry (Simpson & Vakharia, 1999). Apple – Apple’s supply chain combines global sourcing, just-in-time production, and strategic supplier partnerships, allowing it to scale operations efficiently while maintaining quality (Benton, 2020). Walmart – Walmart leverages advanced inventory management systems and distribution technologies to maintain low costs and high product availability, exemplifying the integration of technology into SCM (Monczka et al., 2009). In today’s interconnected and competitive marketplace, Supply Chain Management has become a cornerstone of organisational strategy. By integrating sourcing, procurement, production, and distribution, firms can enhance efficiency, minimise costs, and create sustainable value. With increasing globalisation, technological disruption, and sustainability imperatives, SCM is evolving from a traditional operational function into a strategic enabler of competitive advantage. Organisations that embrace digital innovation, build resilient networks, and commit to sustainability will lead in the next era of supply chain excellence. References Amorim, P. & Almada-Lobo, B. (2014). Combining supplier selection and production-distribution planning in food supply chains. Computer Aided Chemical Engineering, Elsevier. Andersen, P.H. & Rask, M. (2003). Supply chain management: new organisational practices for changing procurement realities. Journal of Purchasing and Supply Management, 9(2), pp.83-95. Benton, W.C. (2020). Purchasing and Supply Chain Management. McGraw-Hill. Chopra, S. & Meindl, P. (2019). Supply Chain Management: Strategy, Planning, and Operation. Pearson. Christopher, M. (2016). Logistics & Supply Chain Management. Pearson UK. Di Pasquale, V., Nenni, M.E. & Riemma, S. (2020). Order allocation in purchasing management. International Journal of Production Research, 58(13), pp.3942–3962. Erengüç, Ş.S., Simpson, N.C. & Vakharia, A.J. (1999). Integrated production/distribution planning in supply chains. European Journal of Operational Research, 115(2), pp.219–236. Giunipero, L.C. & Brand, R.R. (1996). Purchasing’s role in supply chain management. International Journal of Logistics Management, 7(1), pp.29-38. Golini, R. & Kalchschmidt, M. (2011). Moderating the impact … Read more

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